Systems and method for selecting for display images captured in vivo

ABSTRACT

A method executed by a system for selecting images from a plurality of image groups originating from a plurality of imagers of an in-vivo device includes calculating, or otherwise associating, a general score (GS) for images of each image group, to indicate the probability that each image includes at least one pathology, dividing each image group into image subgroups, identifying a set Set(i) of maximum general scores (MGSs), a MGS for each image subgroup of each image group; and selecting images for processing by identifying a MGS|max in each set S(i) of MGSs; identifying the greatest MGS|max and selecting the image related to the greatest MGS|max. The method further includes modifying the set Set(i) of MGSs related to the selected image, and repeating the steps described above until a predetermined criterion selected from a group consisting of a number N of images and a score threshold is met.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to National Phase U.S. patentapplication Ser. No. 16/302,130, filing date May 18, 2017, which claimspriority to PCT International Application No. PCT/IL2017/050559, filingdate May 18, 2017, which claims priority to U.S. Patent Application No.62/338,032, filed May 18, 2016, which are hereby incorporated byreference.

FIELD OF THE INVENTION

The present invention relates to a method and system for selectingimages for display from a series of images captured in vivo. Morespecifically, the present invention relates to systems and methods forselecting and displaying a number of images of interest captured in-vivoaccording to a predefined ‘budget’ of images set in advance.

BACKGROUND OF THE INVENTION

Capsule endoscopy (“CE”) provides thousands, for example 100,000 to300,000, of images of the GI tract per imaging procedure. Reviewinglarge numbers of images is time consuming and inefficient, and, inaddition, having to review so many images may deter some physicians evenfrom trying to do such image reviews. This is so because, when viewing amovie, for example a moving image stream which may be used for medicaldiagnosis, a viewer may want to view only certain frames, or may wish toview a short preview only, summarizing only specific frames which are ofparticular interest (e.g., according to a pre-set criteria), whileskipping other images that are unimportant or less important. Forexample, an in-vivo imager may capture, for example, 150,000 imagesduring a procedure, only some of which may include polyps anchorbleeding sites and/or other pathologies within the gastrointestinal(“GI”) tract. For example, polyps, ulcers, diverticulum and bleeding arefocal pathologies that can appear only in a few images and, therefore,they might be missed/skipped by the physician if ‘hidden’ in a verylarge number of images.

It would be beneficial to highlight to a user, for example a physician,a relatively small set of images that contain the most pathologicallyconspicuous images, to, thus, save a lot of review time and effort. Itwould also be beneficial to reduce the number of images, but, at thesame time, ensuring that all pathologies in the GI tract, or in asegment thereof, are represented (have at least one representativeimage) in the smaller set of images.

SUMMARY OF THE INVENTION

In some embodiments, a method for selecting images for processing fromimages captured by an in-vivo device may include performing, for a groupof contiguous images captured in a body lumen (with each image isassociated a general score, GS, indicative of a probability that theimage includes at least one type of pathology): dividing the group ofcontiguous images into subgroups of images according to a predeterminedcriterion (e.g., similarity between whole successive images, a same‘object’ appearing in successive images, etc.), selecting an image froma particular image subgroup whose general score, GS, is the greatest inthe image subgroup, nullifying the GSs related to the images near theselected image, and repeating the selection and nullification processfor the remaining non-nullified GSs until a number N of images isselected, or until the value of an identified greatest GS is lower thana score threshold. (An image subgroup may be formed or created forexample by analyzing resemblance between images, and a greatest GS inthe subgroup may be identified and its image selected, or a greatest GSmay be found, and images may be grouped ‘around’ the image related tothe greatest GS.) Contiguous images may be for example images which areimmediately next to each other in a stream when ordered chronologically,for example according to the time of their capture.

In some embodiments, a method for selecting images processing mayinclude: (i) identifying a greatest GS among the GSs of the images,selecting an image related to the greatest GS and nullifying thegreatest GS; (ii) identifying an object in the selected image; (iii)searching for the object which is the same as the identified object inimages near the selected image; and (iv) nullifying the GSs related tothe images near the selected image, and repeating or iterating steps(i)-(iv) for all the non-nullified GSs until a number N of images isselected, or until the value of all identified greatest GS is lower thana score threshold.

In these embodiments the object in the selected image may be selectedfrom the group consisting of: (1) an entire image, in which case it maybe determined (e.g., by a processor) that a selected image and imagesnear the selected image make up an image subgroup if there is similarity(resemblance) between these images, and (2) an imaged object (e.g.,pathology), in which case it may be determined (e.g., by a processor)that a selected image and images near the selected image make up animage subgroup if these images include, for example, a same or identicalimaged object (e.g., a same pathology, for example a same polyp). Inthese embodiments, the step of searching for the same object in imagesnear the selected image may include tracking the object backward (inimages preceding the currently selected image) and forward (in imagessubsequent to the currently selected image). In one embodiment‘backward’ (and preceding) and ‘forward’ (and subsequent) refer to theorder of images in the portion of image stream making up the group ofcontiguous images based on, for example, time of capture of an image orother ordering. Thus ‘backward’ may be, for example, looking to imagesbefore the current image in time, or time of capture, or ordering in animage stream, and forward may be the reverse.

In some embodiments a method applied to a group of contiguous imagescaptured in a body lumen by using one imager, where each image may beassociated with a general score (“GS”) indicative of a probability thatthe image includes at least one type of pathology, and a distance, d,indicative of a distance between images, may include: dividing, orseparating, the group of contiguous images, or a portion thereof, intoimage subgroups, where each image subgroup includes a base image andimages resembling to the base image; identifying a maximum general score(“MGS”) for each image subgroup, and selecting images for processing,the selecting may include: (i) identifying the greatest MGS (MGS|max) inthe MGSs identified for the image subgroups and selecting the imagerelated to or having the greatest WIGS; (ii) nullifying the MCS relatedto or having the selected image; (iii) modifying each MGS based on adistance d between an image related to or having the particular MGS andeach selected image; (iv) identifying, in the modified MGSs, a modifiedMGS which has the maximum value (MGS|max). Embodiments may furtherinclude selecting the image that is related to or having that modifiedMGS; and repeating steps (ii)-(iv) until a predetermined criterionselected from a group consisting of a number N of images and a scorethreshold is met. The N selected images, or a subset of the N selectedimages, may be, for example, displayed on a display device for a user.The MGS in each image subgroup may be a local maxima general score inthe image subgroup.

Modifying each particular GS (e.g., MGS) may be based on a distance, d,between the image related to, or having, the particular GS (or MGS) anda nearest selected image. Modifying the particular CS (or MCS) may beperformed by identifying a minimum distance, Dmin, between the imagerelated to or having the particular GS (or MGS) and any of the selectedimages, and modifying the particular GS (or MGS) using the minimumdistance, Dmin. (The smaller Dmin, the greater a reduction in the valueof the particular CS or MGS.) The distance, d, between an image relatedto or having a GS (or MGS) and a selected image may be calculated ormeasured in units of: (i) time, or (ii) a number of intervening images,or (iii) a number of intervening image subgroups, or (iv) a distancethat the in-vivo device travelled in the body lumen while taking theimages, or (v) a distance that the in-vivo device travelled in the bodylumen with respect to a landmark in the group of images or in the bodylumen. Each unit instance may be calculated or measured as: (i) anabsolute value calculated or measured relative to a beginning of thegroup of contiguous images, or relative to a beginning of the bodylumen, or relative to a landmark in the group of contiguous images or inthe body lumen, or as (ii) a percentage or fraction of the group ofcontiguous images or body lumen, or (iii) a percentage or fraction of avideo clip including all, or most of the group of contiguous images, or(iv) a percentage or fraction of a distance travelled by the in-vivodevice, or (v) a percentage or fraction of a time travelled by thein-vivo device.

Modifying GSs (e.g., MGSs) may include applying a modification functionto the GS or MGSs with respect to a selected image. The modificationfunction may be selected from a group consisting of: symmetricalfunction, asymmetrical function, linear function, non-linear, Gaussianexponent, exponent with an absolute value of distance, a step function,and a triangularly shaped function with its apex located at a selectedframe and linearly decreases with distance. The modification functionmay be selected according to a parameter selected from a groupconsisting of: type of pathology, location in the GI tract and velocityof the in-vivo device in the body lumen.

Embodiments may include applying a modification function to GSs or MGSsbased on a location in the body lumen at which the images related to theMGSs were captured. The body lumen may be the gastrointestinal tract,and a first modification function may be applied to GSs or MGSs relatedto images captured in the small bowel, and a second modificationfunction may be applied to GSs or MGSs related to images captured in thecolon.

Embodiments may include zeroing out (e.g., setting to zero) a generalscore of an image if the image is noisy. The image may be regarded asnoisy if it contains bubbles and/or gastric content and/or if the imageprovides tissue coverage below a tissue coverage threshold value and/orthe image complies with a darkness criterion.

An image general score may be calculated by using a number m of scorevalues, S1, . . . , Sm, respectively outputted by m pathology detectors,the m score values indicating probabilities that the image includes in,or k (k<m), types of pathologies. A pathology may be selected from thegroup consisting of: a polyp, a bleeding, a diverticulum, an ulcer, alesion and a red pathology.

In other embodiments a method applied to q groups of contiguous imagesrespectively captured in a body lumen by q imagers, with each image ofeach group are associated a general score (GS) indicative of aprobability that the image includes at least one type of pathology, anda distance, d, indicative of a distance between images of the respectivegroup, may include: for each image group of the q groups of contiguousimages, performing: (A) dividing, or separating, the image group, or aportion thereof, into image subgroups, each image subgroup comprising abase image and images resembling to the base image, and (B) identifyinga set Set(i) (i=1, 2, 3, . . . ) of maximum general scores (MGSs), theset Set(i) of maximum general scores comprising a maximum general score(MGS) for each image subgroup of the image group; and selecting imagesfor processing by performing: (i) identifying a maximum MUS (MGS|max) ineach set S(i) of MGSs; (ii) identifying, among all the MGSs|max, thegreatest MGS|max, and selecting the image related to the greatestMGS|max; (iii) nullifying the greatest MGS|max related to the selectedimage; (iv) modifying the particular set Set(i) of MGSs related to theselected image based on a distance d between images respectively relatedto the MGSs of the particular set Set(i) of MGSs and each selectedimage; and repeating steps (i)-(iv) until a predetermined criterionselected from a group consisting of a number N of images and a scorethreshold is met.

The in-vivo device may include a number of imagers selected from one andtwo, and modifying a particular set Set(i) of MGSs related to a selectedimage originating from a first imager may include modifying MGSs of theparticular set Set(i) also based on images originating from the secondimager.

A system for selecting images from images captured by a number q ofimagers of an in-vivo device may include a storage unit for storing qgroups of contiguous images respectively captured in a body lumen by qimagers, and for storing, in association with each image of each group,a general score (GS) indicative of a probability that the image includesat least one type of pathology, and a distance, d, indicative of adistance between images of the respective group, and a data processor.The data processor may be configured to, for each image group of the qgroups of contiguous images: (A) divide, or separate, the image group,or a portion thereof, into image subgroups, each image subgroupcomprising a base image and images resembling to the base image, and (B)identify a set Set(i) (i=1, 2, 3, . . . ) of maximum general scores(MGSs), the set Set(i) of maximum general scores may include a maximumgeneral score (MGS) for each image subgroup of the image group. The dataprocessor may be also configured to select images for processing by: (i)identifying a maximum MGS (MGS|max) in each set S(i) of MGSs; (ii)identifying, among all the maximum MGSs, the greatest MGS|max, andselecting the image related to the greatest MGS|max; (iii) nullifyingthe greatest MGS|max related to the selected image, and (iv) modifyingthe particular set Set(i) of MGSs related to the selected image based ona distance d between images respectively related to the MGSs of theparticular set Set(i) of MGSs and each selected image, and to repeatsteps (i)-(iv) until a predetermined criterion selected from a groupconsisting of a number of N images and a score threshold is met.

BRIEF DESCRIPTION OF THE DRAWINGS

The principles and operation of the system and method according to thepresent invention may be better understood with reference to thedrawings, and the following description, it being understood that thesedrawings are given for illustrative purposes only and are not meant tobe limiting, wherein:

FIG. 1 shows an in-vivo imaging system according to an embodiment of thepresent invention;

FIG. 2 diagrammatically illustrates an image scoring scheme according toan embodiment of the present invention;

FIG. 3 diagrammatically illustrates an image grouping scheme accordingto an embodiment of the present invention;

FIGS. 4A and 4B show example scoring graphs according embodiment of thepresent invention;

FIG. 4C and FIG. 4D respectively show manipulation of scoring in thescoring graphs of FIG. 4A and FIG. 4B according to an embodiment of thepresent invention;

FIGS. 5A-5F demonstrate a scores modification process according to anexample embodiment of the invention;

FIG. 6A shows a method for selecting images originating from one imageraccording to an example embodiment;

FIG. 6B shows a method for selecting images originating from one imageraccording to another example embodiment;

FIG. 6C demonstrates calculation of distances between images accordingto an example embodiment;

FIG. 7 shows a method for selecting images originating from two imagersaccording to another example embodiment;

FIG. 8 schematically illustrates an example, implementation of the imageselection method according to an example embodiment;

FIG. 9 shows a method for selecting images originating from one imageraccording to another example embodiment of the invention; and

FIGS. 10A-10C demonstrate a scores modification process in accordancewith the embodiment shown in FIG. 9 .

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions and/or aspect ratio of some of the elementsmay be exaggerated relative to other elements for clarity. Further,where considered appropriate, reference numerals may be repeated amongthe figures to indicate the same, corresponding or analogous elements.

DETAILED DESCRIPTION OF THE INVENTION

In the following description, various aspects of the present inventionwill be described. For purposes of explanation, specific configurationsand details are set fourth in order to provide a thorough understandingof the present invention. However, it will also be apparent to oneskilled in the art that the present invention may be practiced withoutthe specific details presented herein. Furthermore, well known featuresmay be omitted or simplified in order not to obscure the presentinvention.

Discussions utilizing terms such as, for example, “processing,”“computing,” “calculating,” “determining,” “establishing”, “analyzing”,“checking”, or the like, may refer to operation(s) and/or process(es) ofa computer, a computing platform, a computing system, or otherelectronic computing device, that manipulates and/or transforms datarepresented as physical (e.g., electronic) quantities within thecomputer's registers and/or memories into other data similarlyrepresented as physical quantities within the computer's registersand/or memories or other information non-transitory storage medium thatmay store instructions to perform operations and/or processes.

Unless explicitly stated, the operations described herein are notsubjected to a particular order or sequence. For example, some methodsteps can occur or be performed simultaneously, at the same point intime, or concurrently, and some method steps can occur or be performedin reversed order.

Embodiments of the systems and methods of the present invention may beused in conjunction with an imaging system or device capable ofobtaining images of in-vivo objects. Any imaging device or system thatcan be incorporated into an in-vivo device may be used, and embodimentsof the invention are not limited by the type, nature or other aspects ofthe imaging system, device or unit used. Some embodiments of the presentinvention are directed to a swallowable in-vivo device, such as anautonomous swallowable capsule. However, the in-vivo device need not beswallowable or autonomous, and may have other shapes or configurations.

An in-vivo imaging device may capture a series of images as it traversesthe GI system, and transmit images, typically by transmitting one imageframe at a time. The images may be later compiled at or by a receiver toproduce a displayable video clip or an image stream, or a series ofimages. An image transmitted by the in-vivo imaging device to thereceiver may be a color image. In one example, each image may include256 rows of 256 pixels each, where each pixel may be represented, or hasassociated therewith, binary data (e.g., bytes) that may represent, forexample, the pixel's color and brightness.

Detection of an anomaly in a tissue imaged by an imaging device mayinclude measuring one or more pixel parameters (e.g., color parameter)of one or more pixels in an image. An anomaly detected for display maybe any anomaly, e.g., a polyp, a lesion, a tumor, an ulcer, a bloodspot, an angiodysplasia, a cyst, a choristoma, a hamartoma, a tissuemalformation, a nodule, to list some anomalies.

Embodiments of the present invention are directed to methods andprocesses for selecting, for example for further processing and/or foranalysis and/or for display, a set of relatively small number, N, ofimages (e.g., 100 images), which is referred to herein as an ‘imagebudget’ or limit, such that each image in the group of selected imagesincludes or represents at least one pathology, and the probability thatthe group of selected images would include multiple images of a samepathology would be minimized in order not to waste the image budget onsimilar or redundant images. In some embodiments of the presentinvention, the selected images may be displayed to a user ‘as is’ (e.g.,without further processing). In some other embodiments of the presentinvention, a selected image may be further processed, where theprocessing, or further processing, of the selected image may includeanalysis of the selected image, for example to further investigate thepathology with more robust software tools, for example to corroborate orto refute the initial determination that the image includes thepathology, or to assess the severity of the pathology. A selected image,in conjunction with other, non-selected, images belonging to the samesubgroup as the selected image, may be used in the preparation ofthree-dimensional presentation of the pathology. Further processing of aselected image may include emphasizing a particular area in the image ina way suitable for the pathology, or pathologies, identified in theimage, for example to emphasize a pathology, so that the viewer can seeit instantly, without having to waste time on searching for iteverywhere in the image.

Embodiments of the methods and process for selecting the images mayinclude a phase of applying one or more pathology detectors (D1, D2, . .. ,) to images in order to score them in terms of the probability thatthey include one or more pathologies, and another phase that includesiteratively or repeatedly selecting images for display, one image at atime, using an ‘image budget’, or a pathology related score threshold,or a combination of the image budget and the pathology related scorethreshold, or any other suitable criterion. Using pathology detectorsmay be optional, as pathology scores may be calculated by some othersystem (e.g., by a remote computer), and the system and methods subjectof the present invention may receive scores from the other system asinput and perform the image selection methods based on the receivedscores. Each time an image is selected for display or for furtherprocessing (for example), image scores are modified (‘suppressed’,reduced in value) in a way that increases the probability that otherimages that also include clinically important information, though ofother pathologies or in other locations or parts in the body lumen,would also be selected for display. ‘Suppressing a score’ means hereinreducing the value of the score by an amount calculated by using a scoremodification function.

Some embodiments disclosed in the present invention are beneficial to GIimaging because for example they present to a user (e.g., a physician) asmall number of images that includes only images which are the mostclinically important. In addition, some embodiments disclosed hereinenable the user (e.g., the physician) to select any number of importantimages the user may want to review (e.g., 20 images, or 50 images,etc.), or to apply the image selection process to a particular type ofpathology (e.g., polyps), or to a particular location in the GI tract(e.g., colon) while limiting the total number of images including theparticular pathology, or including all pathologies in the particularlocation, to a ‘reasonable’ or ‘convenient’ number. That is, instead ofhaving to review thousands of images indiscriminately, a user may useembodiments disclosed herein to review as many images as s/he wants,with each image showing a pathology, and to direct the image selectionprocess to any desired pathology and/or to any desired segment or partof the GI tract, Therefore, embodiments disclosed herein may give a user(e.g., physician) flexibility, for example, in terms of: (1) setting thenumber of images to be displayed, or further processed, to a desiredvalue, (2) selecting the type of pathology or pathologies that theselected images will show, and (3) selecting the location, or locations,in the GI tract from which images will be selected, for display, or forfurther processing.

The term “object”, as used herein, may refer, in some embodiments, to anentire image, for example it may refer to image characteristics, or toimage features, that characterize an image as a whole. In otherembodiments the term “object” may refer, for example, to an imagedobject (that is, an object whose image appears in an image). An imagedobject may be, but not limited to, for example, a polyp, a lesion, ableeding site, a diverticulum, an ulcer, a pathology, etc.

An imaged object (e.g., a polyp) that is contained in a selected imagemay be small, medium or large relative to the size of the image, and itmay be located anywhere in the image and have any angle of rotation withrespect to the image. If the imaged object contained in the selectedimage (the ‘original’ object) is also contained (also shown) a nearimage, a processor may determine (e.g., by using any suitable objectcharacterization algorithm) that the imaged object in the near image andthe original object in the selected image are the same or identicalimaged object even though the size and/or location and/or the angle ofrotation of the imaged object contained in the near image are nurespectively different than the size and/or location and/or the angle ofrotation of the original object. That is, two images may be regarded assimilar by or because of their imaged object even though an imagedobject in one image is translated or rotated, or has a different size,with respect to the imaged object in the other image.

The term “image subgroup”, or “subgroup” for short, as used herein,refers, in some embodiments, to a collection of near images that,together with a related selected image, make up or form a group due totheir image characteristics being similar and, therefore, these imagesmay resemble each other through “similarity”. In other embodiments theterm “subgroup” refers to a collection of near images that make up orform a group due to their including a same imaged object (or a same‘object’, for short). Accordingly, after an image is selected (by usingany image selection method that is described herein), an image subgroupmay be defined, according to some embodiments, with respect to theselected image, by searching for, and finding, near or nearby imagesthat are similar to the selected image (e.g., that have similar imagecharacteristics as the selected image), or, in other embodiments, bysearching for, and finding, near or nearby images that include a same(imaged) object as the (imaged) object included in the selected image.

That is, if a selected image contains an object e.g., a polyp), thisobject is ‘tracked’ back, in images preceding the selected image, andforward, in images subsequent to the selected image. The same specificobject (e.g., a particular polyp) that is found in a selected image maybe found in one or more preceding images and/or in one or moresubsequent images, or it may appear (be contained in) only the selectedimage. (An image subgroup may include only one image; e.g., the selectedimage.)

FIG. 1 shows a block diagram of an in-vivo imaging system according toone embodiment of the present invention. The system may include anin-vivo imaging device (e.g., imaging capsule) 40. Device 40 may beimplemented using a swallowable capsule, but other sorts of devices orsuitable implementations may be used. Device 40 may include one or moreimagers 46 (e.g., two imagers—one on each side of device 40) forcapturing images in a body lumen (e.g., in the GI tract). Device 40 mayalso include an illumination source (or sources) 42 for illuminating abody lumen, for example in the GI tract of a subject, and atransmitter/receiver 41 for transmitting images in the form of dataframes) captured in vivo by imager(s) 46. Device 40 may also include anoptical system including, for example, lenses, to focus reflected lightonto imagers) 46.

Preferably, located outside the patient's body in one or more locationsis a receiver 12 to receive, for example, images that device 40transmits. Receiver 12 may include an antenna or an antenna array thatmay releasably be attached to the patient. Receiver 12 may include astorage unit 16 (e.g., a computer memory, or a hard disk drive) forstoring, for example, all or selected images that nu receiver 12receives from device 40. Receiver 12 may also store metadata related toimages that it receives from device 40, such as, for example, timeinformation sent by device 40 and indicates the time at which device 40captures each image and/or the time each image is received at receiver12. (As described below, for example in connection with FIG. 6C, suchtime information may be used by a processor to determine (e.g.,calculate), in this example temporal, distances between images asprerequisite to modification (suppression) of image general scores.)Preferably, receiver 12 is small and portable, and is wearable on thepatient's body during receiving (and recording) of the images.

The in-vivo device system may also include a workstation 13 to whichreceiver 12 may transfer the imams originating from device 40.Workstation 13 may include, or be connected to, an image display device18 for displaying, among other things, images originating from device40. Workstation 13 may receive image data, and optionally other type ofdata, from receiver 12, and process the image data in order toautomatically display, for example on display device 18, images (and/ora video clip including a series of images) that were captured by device40, Workstation 13 may enable a user a physician) to, for example,select images for display, or to enhance displayed images. Workstation13 may enable the user, for example by mouse-clicking, pressing ortapping a button, to commence the image selection process disclosedherein, such that a relatively small, predetermined or configurable,number, N, of images (for example N=100 images) showing (containing) themost clinically important or conspicuous images would be automaticallydisplayed on display device 18 for the physician, rather than expectingthe physician to review hundreds or thousands of images, trying to findthe important images on her/his own. Workstation 13 may enable the userto select the value of N. Workstation 13 may enable the user to limitthe image selection process to a particular part of the body lumen;namely, to select only images that are captured in a particular part ofthe body lumen, for example in a particular part of the GI tract (e.g.,in the small bowel). In some embodiments, the number (N) of selectedimages is not set in advance but, rather, is determined as, or derivablefrom, a result of using another criterion. For example, a scoringthreshold mechanism may be used in the selection of images, asdescribed, for example, in connection with FIG. 7 , which is describedbelow. In some embodiments, the number (N) of selected images and ascoring threshold value or mechanism may be used in combination. Forexample, a scoring threshold value or mechanism may be used as a ‘main’criteria, and the number N may be used as a high limit to the number ofimages that are ultimately selected.

Workstation 13 may include a storage unit 19 and a data processor 14.Storage unit 19 may store q groups of contiguous images respectivelycaptured in a body lumen by q imagers (46). Storage unit 19 may alsostore, in association with each image of each group, a general score(GS) that indicates a probability that the image includes at least onetype of pathology, and a distance, d, that indicates a distance betweenimages of the respective group.

Storage unit 19 may include an image storage 20 for storing image data,a scores storage for storing pathology scores and general scorescalculated for images, and a modification function storage 24 forstoring one or more modification functions for modifying scores. Scoresstorage 22 may also store modified scores (scores modified by themodification function(s)). Workstation 13 may include a number in ofpathology detectors 15 (e.g. designated as D1, D2, . . . , Dm, torespectively detect m different, like or similar pathologies/anomalies(e.g., focal pathologies). A pathology detector Di (i=1, 2, . . . , m),which may be or include, for example, a pattern recognition classifier,applied to image data may output a (pathology) score representing theprobability that the image includes (shows) a pathology of a respectivetype. The in pathology detectors (15) may not be required because theimage pathology scores calculated for images may be calculated elsewhere(e.g., by a remote computer) and be transferred, for example, toworkstation 13. A pathology detector may analyze various parametersand/or features in or associated with an image (e.g., intensity level ofpixels, color tones, amplifier gain, light exposure time, etc.), andidentify features representative or characteristic to the pathology typeinvolved, and, using these features, a classifier, which may be part ofthe pathology detector, may output a value (e.g., score) indicating theprobability that the image includes the pathology.

An image general score may be calculated by using a number in of scorevalues, S1, . . . , Sm, respectively outputted by m pathology detectors,where the m score values indicate probabilities that the image includesin, or k (k<m), types of pathologies. A pathology may be a polyp, ableeding, a diverticulum, an ulcer, a lesion, a red pathology, etc.(Other pathology types may be detected or identified in images andsimilarly handled.) An image may include more than one pathology.

Data processor 14 may be configured to carry out embodiments of theinvention for example by executing software or code stored for examplein storage 19. In some embodiments, detectors 15 or other classifiers,modules, etc. described herein may be, or may be carried out, by dataprocessor 14 executing such code.

In some embodiments, data processor 14 may apply different modificationfunctions to pathology scores related to images that were captured indifferent parts of the body lumen. For example, modification, orsuppression, of pathology scores related to images that are captured ina first location in the body lumen (e.g., small bowel) may be performedby using a first modification function, while modification of pathologyscores related to images that are captured in a second location in thebody lumen (e.g., colon) may be performed by using a second modificationfunction, and so on. Data processor 14 may enable a user (e.g., aphysician) to select a modification function according to the part ofthe body lumen from which images were taken, and this may be beneficialbecause, this way, the user may ‘shift the focus’ to (that is, bias theimage selection process towards) a particular part of the body lumen theuser is more interested in. Shifting the focus to (that is, biasing theimage selection process towards) a particular part of, or area in, thebody lumen may be implemented by, for example, reducing the values ofthe pathology scores related to the focused on lumen part leniently,while rigorously reducing the values of the pathology scores related toother parts of the lumen.

Data processor 14 may process the image data stored in image storage 20,and, in some embodiments, use the m detectors to calculate, for eachimage, various types of pathology scores. Processor 14 may store thevarious scores in score storage 22, and update, from time to time, thescores content of score storage 22. Updating the scores in score storage22 by processor 14 may include, for example, adding new scores, zeroingscores, scaling scores and updating scores by using the modificationfunction(s) that are stored in modification function storage 24.Processor 14 may use the scores calculation and modification(suppression) process to select, for example for display, apredetermined number, N, of the most clinically important imagescaptured by in-vivo device 40. Processor 14 may iteratively orrepeatedly select one such image at a time, and every time processor 14selects an image, processor 14 may store, for example in image storage20, the selected image, or a reference information (e.g., a pointer)that references the selected image. The way processor 14 calculates andmodifies, or suppresses, pathology scores and use them to select the Nclinically most important images is described and exemplifiedhereinafter.

By way of example, data processor 14 may be configured, among otherthings, to perform as described below for each image group of the qgroups of contiguous images:

-   (A) Divide, or separate, the image group, or a portion thereof, into    image subgroups, each image subgroup comprising a base image and    images resembling to the base image, and-   (B) Identify a set set(i) (i=1, 2, 3, . . . ) of maximum general    scores (MGSs), the set Set(i) of maximum general scores comprising a    maximum general score (MGS) for each image subgroup of the image    group; and to select images for processing by: (i) identifying a    maximum MGS (MGS|max) in each set S(i) of MGSs; (ii) identifying,    among all the maximum MGSs, the greatest MCS|max, and selecting the    image related to the greatest MGS|max; (iii) nullifying the greatest    MGS|max related to the selected image; and (iv) modifying the    particular set Set(i) of MGSs related to the selected image based on    a distance d between images respectively related to or having the    MGSs of the particular set Set(i) of MGSs and each selected image.    Data processor 14 may repeat steps (i)-(iv) until a predetermined    number N of images has been selected for display or for further    processing, or until the value of an identified (modified) maximum    MGS (MGS|max) is lower than a score threshold, Sth.

Data processor 14 may be or include any standard data processor, such asa microprocessor, multiprocessor, accelerator board, or any other serialor parallel high performance data processor. Display device 18 may be acomputer screen, a conventional video display, or any other devicecapable of providing image and/or other data.

Preferably, imager 46 may be or include a CMOS camera or another device,for example, a CCD camera. Illumination source 42 may include, forexample, one or more light emitting diodes (LEDs), or another suitablelight source.

During operation, imager 46 captures images and sends data representingthe images to transmitter 41, which transmits images to receiver 12using, for example, electromagnetic radio waves. Receiver 12 transfersthe image data to work station 13 for storage, processing anddisplaying. The in-vivo imager may capture a series of still images asit traverses the GI tract. The images may be later presented byworkstation 13 as, for example, a stream of images or a video clip ofthe traverse of the GI tract. The in-vivo imaging system may collect alarge volume of data, as the device 40 may take several hours totraverse the GI tract, and may record images at a rate of, for example,two images every second, or twenty four images every second, etc.,resulting in the recordation of thousands of images. The image capturingand transmission rate (or frame capture rate, or ‘frames per second’rate) may vary.

Preferably, the image data recorded and transmitted by device. 40 isdigital color image data, although other image formats may be used. Byway of example, each image flume includes 256 rows of 256 pixels each,each pixel including, or represented by, digital bytes whose valuesrepresent, for example, color and brightness, according to knownmethods.

FIG. 2 schematically illustrates an image scoring scheme according to anembodiment of the present invention. FIG. 2 is described in associationwith FIG. 1 . Assume that in-vivo device 40 includes two imagers(“Imager-1” and “Imager-2”), and each imager captured a number k ofimages by the time device 40 was excreted by the patient. (Embodimentsdescribed herein may likewise be applicable to any sequence of images,regardless of the number of imagers involved in their capturing. Forexample, embodiments described herein may likewise be applicable to asequence of images that were captured by an in-vivo device including oneimager) Also assume that each image is analyzed using a number in ofpathology detectors, where each detector is configured to detect adifferent, or similar, pathology in images by calculating a score, Si,for each image such that a score calculated for a particular imageindicates the probability that a GI site captured by the particularimage includes the particular pathology. By way of example, a firstpathology detector may be configured to detect polyps in the colon; asecond pathology detector may be configured to detect bleeding in the GItract; a third pathology detector may be configured to detect ulcers inthe GI tract; a fourth pathology detector may be configured to detectdiverticulum in the GI tract, etc. Alternative or additional pathologydetectors may be used. A particular pathology may be detected by usingmore than one detector. For example, one polyp detector may detect apolyp by detecting its contour line, another polyp detector may detect apolyp by detecting colors patterns characterizing polyps, etc.

Referring to FIG. 2 , Imager-1 captured a group of k contiguous images,designated as Image-1/1 (image #1 of imager-1), Image-2/1 (image #2 ofimager-1), Image-k/1 (image #k of imager-1). Similarly, Imager-2captured a group of k contiguous images, designated as Image-1/2 (image#1 of imager-2), Image-2/2 (image #2 of imager-2), . . . , Image-k/2(image silk of imager-2). Images that are contiguous may be, forexample, images that are immediately precedent or subsequent to eachother in an image stream.

Applying in detectors to Image-1/1 results in in scores (a score perdetector), designated as S1-1/1 (detector 1's score calculated for image1 of Images-1), S2-1/1 (detector 2's score calculated for image 1 ofImager-1), . . . , (detector air's score calculated for image 1 ofImager-1). Similarly, applying the in detectors to Image-2/1 results inm scores (a score per detector), designated as S1-2/1 (detector 1'sscore calculated for image 1 of Imager-1), S2-2/1 (detector 2's scorecalculated for image 1 of Imager-1), . . . , Sm-2/1 (detector m's scorecalculated for image 1 of imager-1), and so on.

Similarly, applying the m detectors to Image-1/2 results in in scores (ascore per detector), designated as S1-1/2 (detector 1's score calculatedfor image 1 of Imager-2), S2-1/2 (detector 2's score calculated forimage 1 of Imager-2), . . . Sm-1/2 (detector m's score calculated forimage 1 of Imager-2). Similarly, applying the fin detectors to Image-2/2results in m scores (a score per detector), designated as S1-2/2(detector 1's score calculated for image 2 of Imager-2), S2-2/2(detector 2's score calculated for image 2 of Imager-2), . . . , Sm2/2(detector m′s score calculated for image 2 of Imager-2), and so on. Eachimage may, therefore, have associated with it in scores thatrespectively indicate probabilities that the image includes (images)various pathologies.

FIG. 3 schematically illustrates an example of image grouping accordingto an embodiment of the present invention. For the sake of simplicity,FIG. 3 shows a group 300 of contiguous images captured by one imager.

An image subgroup (SGi) includes temporally successive images thatresemble each other or are similar to each other (e.g., in terms ofvisual features; configurative features, or computed features; e.g.,ratio between image parameters, for example light intensities, colorlevels, etc.), and, therefore, they may show or include a same pathology(e.g., polyp, bleeding, ulcer, lesion, diverticulum, etc.). However,since the image ‘budget’ (e.g., the limit or maximum number, N) ofimages to be selected for display (for example) is very small (comparingto the total number of thousands or tens of thousands of capturedimages), images that are similar to each other within a same imagesubgroup can be detracted or removed from further processing (e.g.,detracted or removed front the image selection process), leaving only arepresentative image front each image subgroup that represents therespective image subgroup.

To start grouping images (to start partitioning the image group) intosubgroups, a similarity level is checked (310) between a first image,designated as ‘Image-1’, and a temporally contiguous image, designatedas ‘Image-2’. In the example of FIG. 3 , Image-1 mid Image-2 are assumedto be similar. Similarly, similarity between Image-1 and Image-3 ischecked (320). In the example of FIG. 3 , Image-1 and Image-3 are alsoassumed to be similar. Similarly, similarity between Image-1 and Image-4is checked. In the example of FIG. 3 Image-1 and Image-4 are alsoassumed to be similar. Similarly, similarity between Image-1 and Image-5is checked. In the example of FIG. 3 , Image-1 and Image-5 aredissimilar. Therefore, continuing the example, the first image subgroup(SG1) includes four images: Image-1 (image 330, which may be regarded asa base image of image subgroup SG1), linage-2, Image-3 and Image-4. Thesame image grouping process continues for the next image subgroups (SG2)by using image image-5 (image 340) as a new subgroup's base image. Image340 is the first image that does not belong to image subgroup SG1 due toit being dissimilar to the base image 330 of image subgroup SG1Therefore, image 340 is used as a base image of image subgroup SG2, and,by way of example, subgroup SG2 includes three images: Image-5, Image-6and Image-7. Image subgroup SG3 includes image Image-8 as its base image(350), and additional images that are not shown in FIG. 3 . Additionalimage subgroups may be identified in a similar way. Similarity betweenimages may be determined (for example calculated) based on any suitablecriteria, or by using other image grouping methods. For example,similarity between images may be determined based on comparison ofpreselected features or parameters (e.g., light intensity, color(s),etc.), or similarity between images may be determined in a reversedorder, or “backwards”; that is, from a particular image to previouslycaptured images. Images may be grouped using any suitable clusteringmethod.

An image group may be partitioned using other methods. For example, animage group may be partitioned into image subgroups such that each imagesubgroup has the same number of images. In some embodiments, the numberof images of, or included in, an image subgroup may depend on the partor organ of the body lumen (e.g., in the small bowel, or in the colon)the in-vivo device captures the images. To this effect, a location ofthe in-vivo device in the body lumen may be detected from, or determinedbased on, for example, one or more images, or by using an externallocalization system. Then, or concurrently, the location of the in-vivodevice may be transferred to, for example, workstation 13 (FIG. 1 ), andstored, for example, in storage unit 19, in association with the image,or images, that was/were captured in this location. By way of example,data processor 14 may partition or divide images captured in the smallbowel into small image subgroups, and images captured in the colon intolarge image subgroups, or vice versa. (A ‘small image subgroup’ includesa smaller number of images comparing to the number of images of a ‘largeimage subgroup’.)

FIGS. 4A and 4B show example scoring graphs according to an embodimentof the present invention. FIG. 4A and FIG. 4B are described inassociation with FIG. 3 , and they respectively show example scores thatare calculated for image subgroup SG1 by applying two pathologydetectors D1 and D2—on each image of subgroup SG1. (As described herein,for example in connection with FIG. 1 and FIG. 2 , a number in ofdetectors (D1, D2, . . . , Dm) may be applied to each image of an imagegroup in order to calculate, for the image, a number in of scores (S1,S2, . . . , Sm) that respectively indicate the probability that theimage shows, or include, in pathologies of the types respectivelydetectable by the m detectors.)

FIG. 4A shows example scores (S1 s) obtained for image subgroup SG1 byusing pathology detector D1. By way of example, image subgroup SGFincludes four images which are designated in FIG. 4A as ‘1’, ‘2’, ‘3’and ‘4’ on the “Image No.” axis. (The same image numbers are also usedin FIG. 4B.) For example, assume that the scores calculated by detectorD1 for the four images are: S1=90 for image No. 1, S1=105 for image No.2, S1=150 for image No. 3, and S1=124 for image No. 4. FIG. 4B showsexample scores (S2 s) obtained for subgroup SG1 by applying pathologydetector D2. For example, assume that the scores calculated by detectorD1 for the four images are: S2=95 for image No. 1, S2=125 for image No.2, S2=80 for image No. 3, and S2=75 for image No. 4.

After the various scores (e.g., one score per detector per image) arecalculated for each image of each of image subgroups SG1, SG2, SG3, andso on, local maxima score values are identified for each score type andfor each image subgroup. By way of example, score value S1=150, shown at410 (FIG. 4A), is the local maximum of the scores calculated bypathology detector D1 for image subgroup SG1, and score value S2=125,shown at 420 (FIG. 4B), is the local maximum of the scores calculated bypathology detector D2 for image subgroup SG1, An image subgroup mayinclude more than one local maxima score value for a particularpathology detector, and different pathology detectors may producedifferent numbers of local maxima score values for a same imagesubgroup.

The local maxima score values identified in each image subgroup may beprocessed further in the image selecting process, whereas the otherscores (scores which are ‘non-conspicuous’ scores) may be excluded fromthis process. Exclusion of ‘non-conspicuous’ scores (scores that are notlocal maximum scores) may be implemented by, for example, zeroing themout, or by reducing their value to an arbitrary value that is lower thanthe lowest pre-suppressed score value, in order to ensure theirexclusion from the selection process. Score values S1 s and S2 s inFIGS. 4A-4B that are zeroed out are shown in FIGS. 4C-4D as S1's andS2's. For example, the zeroed out version of score value S1 related toimage No. 1 is shown in FIG. 4C, as S1′ (at 430). (In the example shownin FIGS. 4A-4D score values S1's related to images ‘1’, ‘2’ and ‘4’—seeFIG. 4C—and score values S2's related to images ‘1’, ‘3’ and ‘4’ seeFIG. 4D—are zeroed out). Non-conspicuous scores may be excluded from theimage selection process because they may represent images that may beless clinically important (in terms of the pathology associated with thetype of these scores) than the images having the local maximum scorevalues. In some embodiments, one image may be selected from an imagesubgroup even though other images in the image subgroup may be asclinically important as the selected image. After the non-conspicuousscore values related to all score types and images are zeroed out, ageneral image score may be calculated for images by using the localmaxima score values. (A zeroed-out score value has no weight indetermining an image's general score value.)

By way of example, each of FIGS. 4A-4B shows an image subgroup thatincludes only a few images and only one local maxima score value foreach type of score. However, this is only an example—an image subgroupmay include many images (the number of which depends on a similaritydegree between images), and, in addition, several local maxima scorevalues. After all local maxima score values are identified in each imagesubgroup, the non-maxima score values (the other score values) in eachimage subgroup are zeroed out, as exemplified by FIGS. 4C-4D, which aredescribed below, in order to exclude the images producing (associatedwith) them from the images' selection process. As described herein,images of an image subgroup are characterized by having a high degree ofsimilarity (for example, above a predetermined similarity thresholdvalue), so, only the more conspicuous images may be selected for displaywhile many other images in each image subgroup can be removed from theimages' selection process, for potentially being unimportant orredundant, without risking losing clinically important images. Since alloriginal images are saved (e.g., in image storage 20, FIG. 1 ), if aparticular image is ultimately selected (and, for example, displayed;e.g., to a user), the system (e.g., workstation 13, FIG. 1 ) may enablethe user to display also images that precede or follow that image (e.g.,images whose score has been zeroed out). Embodiments of the inventionmay enable the user to use a selected image (for example when theselected image is displayed) to display the related image subgroup inits entirety, or partly, for example by clicking or tapping the selectedimage while it is displayed.

Calculating a General Score (GS) for an Image

If all scores of a particular image are zero, cleaning that none of thepathology detectors D1, D2, . . . , Dm detected any pathology in thatimage, the image is categorically excluded from the images' selectionprocess. However, since these detectors (e.g., classifiers) calculateprobabilities (that pathologies exist), it is likely that the detectorswould output, in such cases, some low, non-zero, score values. In otherwords, a pathology detector may output a low, non-zero, score value foran image even if the image does not include the pathology. In addition,an image may have more than one low, non-zero, score values (a scorevalue per pathology detector). However, as described herein, if aparticular pathology score that is output by a respective pathologydetector for a particular image is not a local maxima score value withinthe image subgroup that includes the particular image, the particularpathology score is zeroed out; e.g., set to zero. Zeroing out all scorevalues that are not local maxima score values in some embodimentsguarantees that only the local maxima score values are factored in inthe image selection process described herein, as they are more likely toindicate pathologies.

If an image has one local maxima score value, the local maxima scorevalue may be used further as the image's general score (GS) associatedwith the image. However, as described herein, images may have or beassociated with more than one local maxima score value. (Some images mayhave at least one local maxima score value for more than one pathologydetector.) Therefore, a general score. GS, may be derived for andassociated with each image from (e.g., calculated based on) all thelocal maxima score values calculated for, or associated with, the image.An image general (pathology) score, GS, may be calculated, for example,using formula (1) or formula (2):GS=f(S1,S2, . . . Sm)  (1)GS=Max{f(S1),f(S2), . . . ,f(Sm)}  (2)where Si (i=1, 2, . . . ,m) is a score value output from pathologydetector number i, and pathology score values S1, S2, . . . , Sm arepathology scores respectively related to in types of pathologies. Eachof pathology score values S1, S2, . . . , Sm may have a value that iseither zero (after undergoing the zeroing out process, or without thatprocess) or a local maxima score value. For example, if m=3 (if imagesare analyzed using three pathology detectors), an image general score,GS, may be calculated using, for example, formula (3):GS=Max{W1×S1,W2×S2,W3×S3}  (3)where S1, S2 and S3 are pathology scores respectively related to threepathology detectors, and W1, W2 and W3 are ‘weights’ respectivelyrelated to the three types of pathologies.

An image may have more than one non-zero pathology score, the variousscores being associated with more than one type of pathology. In such acase, the image may include a ‘dominant’ pathology; namely, a pathologythat is visually, or otherwise, more conspicuous than the otherpathologies included in that image. Therefore, given a stringent imagebudget, only the clinically most significant images, which are usuallyimages with the highest pathology scores, are selected, for display.(Selection of an image may be performed regardless of which type ofpathology in the image has the highest pathology score.) Therefore, eachimage is given or associated with a general pathology score (GS) thatrepresents, or is more ‘biased’ towards, the more visually, orotherwise, a conspicuous pathology, in order to increase the probabilitythat the image would be selected for display. (Formulas (1)-(3) may bedevised to obtain that goal.) It may not matter whether a particularimage is selected for display because it has a higher pathology scorefor some pathology comparing to a pathology score for some otherpathology, because, once selected for display, the image would showevery pathology that is included in that image, not only the pathologyhaving the highest pathology score.

In some embodiments, knowing the type of the pathology score (knowingthe pathology type) that is the reason for the selection of an image maybe utilized to enhance the displayed image (e.g., highlight thepathology). For example, it an image is selected for display because itincludes a focal pathology (e.g., a polyp), enhancing the image displaymay include, for example, identifying a polyp's center and overlaying avisually conspicuous cross (‘x’) on the polyp's center and/or overlayinga visually conspicuous circle (e.g., red circle) around the polyp inorder to draw the viewer's (e.g., physician's) attention to the polyp,rather than having the viewer spending time searching for it in theimage. In another example, if an image is selected for display becauseit includes a fragmented or distributed pathology, enhancing the imagemay include overlaying a visually conspicuous boundary line (e.g., acircle, a box, etc.) on the fragmented/distributed pathology, orhighlighting the whole image without committing to the exact location ofthe pathology in the image, in order to draw the attention of the viewer(e.g., physician) to the pathology, rather than having the viewerspending time searching for it in the image.

FIGS. 5A-5E demonstrate an image selection process according to anexample embodiment. FIG. 5A shows an example image group 500 includingtwenty eight contiguous Images captured by an in-vivo device includingone imager, and their respective general scores. (The twenty eightimages are shown ordered temporally, according to the time of theircapture, with image number ‘1’ captured first and image number ‘28’captured last.) In practice, the number of images in an image group maybe very large (e.g., tens of thousands), however the small number ofimages shown in FIG. 5A is used only for illustrative purpose. FIG. 5Aalso shows image group 500 partitioned into three image subgroups (SGs),designated as SG1, SG2 and SG3. Images of an image group may be capturedat a rate that may be as low as, for example, two images per second, orat a much higher rate, for example, 74 images per second. The imagesselection methods disclosed herein are not limited to, or by, anyparticular image capturing rate; the image selection methods disclosedherein are applicable to any image capturing rate.

Assume that the images of image group 500 were analyzed by two pathologydetectors (e.g., polyp detector and bleeding detector), and that ageneral (pathology) score, GS, was calculated for each image using anyof formulae (1)-(3) based on the pertinent two scores output by the twopathology detectors. Also assume that filtering detectors (e.g., contentdetector, bubble detector, tissue coverage detector, etc.) were used todetect images that show, for example, content and/or bubbles, etc., insome site(s) of the GI tract, and assume that such images are assigned ascore value zero (cf. image numbers 8, 12, 19 and 23 in FIG. 5A) inorder to exclude them from the image selection process.

After image group 500 is processed in the way described herein, forexample, in connection with FIG. 3 and FIGS. 4A-4D, the images arepartitioned or divided, or separated, in this example, to three imagesubgroups SG1, SG2 and SG3 based on similarity between images. (For thesake of simplicity, image group 500 includes twenty eight images andthree image subgroups, and the images budget (the number of images toselect, for example, for further analysis and/or display), is three.)

After the images are partitioned or divided, or separated, to imagesubgroups, all local maxima general score (MGSs) values may beidentified in each image subgroup. (The MGSs are shown circled in FIG.5A.) For example, there are two MGS values in image subgroup SG1 (MGSvalues 80 and 90, which are respectively shown at 530 and 540, arerespectively related to images 4 and 7), three MGS values in imagesubgroup SG2 (MGS values 71, 79 and 82, which are respectively shown at550, 560 and 570, are respectively related to images 16, 18 and 20), andone. MGS value in image subgroup SG3 (MGS value 78, which is shown at580, is related to image 25).

In some embodiments, every MGS in every image subgroup may be used(participate) in the image selection process, while the other scorevalues may be ‘suppressed’ by, for example, zeroing out; e.g., settingto zero, their value. In FIG. 5B, every MGS in each of image subgroupsSG1 to SG3 is used in the image selection process, while the values ofthe other (non-local maxima) scores of FIG. 5A are zeroed out. In otherembodiments, only the MGS having the greatest value among the MGSs ineach image subgroup is used in the image selecting process, whereas theother scores (other MGSs and the non-local maxima score values alike)are zeroed out.

FIG. 5C shows a score vector 502 that includes every MGS of each imagesubgroup: the two MGSs that are related to image subgroup SG1, the threeMGSs that are related to image subgroup SG2, and the one MGS that isrelated to image subgroup SG3. (By ‘vector’ is meant herein an orderedset of values.) The score vector may include only MGSs that are thegreatest in the respective image subgroups, for example only MGS=90 fromimage subgroup SG1, only MGS=82 from image subgroup SG2 and the one MGS(MGS=78) from image subgroup SG3.) Score vector 502 may also include, orhas associated with its MGSs content, information regarding the relativelocation of the images (for example in the image group 500) that arerelated to (that resulted in) the MGSs that are stored in score vector502. (Each image location may be associated with the related MGS.) Byway of example, the two MGS values 90 and 71 (shown at 504 and 506,respectively) are adjacent in score vector 502. However, distancesbetween MGSs do not necessarily translate, into distances between theimages related to these MGSs. For example, while two MGSs may beadjacent in score vector 502, their related images may be far from eachother (e.g., a large number; e.g., 35, of images may intervene (e.g. belocated in-between in an ordered image stream) between the imagesrelated to these MGSs). So, with each MGS may be associated locationinformation that may: (1) indicate a location of the related image, and(2) enable to calculate a distance, d, which may be measured; e.g., astime (e.g., as differences between image capturing times), asintervening images, or other measures of distance between any twoimages. An image subgroup may include, have associated with it orresults in more than one maximum (pathology) score, as each pathologydetector may produce one maximum score for each image subgroup. In someembodiments, if an image subgroup has two or more maximum scores, onlythe highest maximum score (that is, only the maximum score having thehighest value among the two or more maximum scores) is used as an imagegeneral score (GS) in the image selection process. This way, only oneimage can be selected from each image subgroup. For example, (referringagain to FIGS. 4A-4B), the image subgroup SG1 includes two maximumscores maximum score 410 and maximum score 420, and only maximum score410 may be selected for the image selection process, due to its value(150) being higher than the value (˜125) of maximum score 420.

In some embodiments, score vector 502 may have a length (in terms of thenumber of the vector elements) corresponding to the number of images inthe image group. (Score vector 502 may contain a score value for eachimage in the image group, though some score values may be local maximascore values while other scare values may be zero, for example as aresult of the scare values zeroing out process described herein andexemplified, for example, in FIG. 5B, or if the images related to thesescore values were detected as including, for example, content orbubbles, or as not showing enough tissue, etc.)

In some embodiments, score vector 502 may contain all the MGSsidentified in each image subgroup, or only the greatest MGS from eachimage subgroup. Since MGSs may be identified one WIGS at a time, thelength of scores vector 502 may change (lengthened) by one vectorelement each time a new MGS is identified in each image subgroup.(Depending on the value of the pathology score(s) calculated by thepathology detector(s) for each image, an image subgroup may include oneMGS or multiple MGSs.

With each MGS that is added to score vector 502 may be associatedlocation information regarding the location of the related imagerelative to the locations of the other images in the image group, orrelative to a reference point. The reference point may be, for example,temporal (e.g., capture time of a particular image), or a physicallandmark in or related to a body lumen from which the images are taken.A location of an image relative to the location of other images may bedetermined (e.g., calculated) based on, for example, the time each imageis captured, or based on a serial number of the orderly captured images,where the first image that is captured may be assigned serial number one(for example), the second image that is captured may be assigned serialnumber two, and so on. (Other image discrimination schemes may be used.)

The relative location of images in the image group may be used, asdescribed herein, to determine (e.g., calculate) distances betweenimages (e.g., in terms of time differences, in terms of the number ofintervening images, etc.). Distances between images are used, asdescribed herein, to variably (e.g., in de-escalating way) modify(suppress) MGSs in order to reduce the chance that images near analready selected image would be selected for display (for example), and,at the same time, to somewhat increase the chance that ‘remote’ images(relative to the selected image) would still be selected even thoughtheir original score values (MGSs) may be relatively low. (The term‘remote’ is used herein in relation to an already selected image.)

After an image is selected (for example for further analysis and/or fordisplay), the MGSs of the other images may be variably modified, orsuppressed, such that the farther an image is from a selected image, themore moderately its score value is lowered (suppressed), hence thede-escalating nature of the score values modification process withrespect to each selected image. While a distance between an imagerelated to a particular MGS and some selected image may be relativelylong, a distance between the image related to the particular MGS andanother selected image (if more than one image have already beenselected) may be shorter. In such a case, the particular MGS may bemodified rigorously using the shorter distance, rather than modifying,or suppressing, it moderately using the longer distance, the reason forthis being that it may be desired to lower the chance that the imagerelated to the particular MGS (which the shorter distance, Dmin.,suggests that it resembles to the nearest selected image) would beselected.

FIGS. 5D-5F demonstrate the effect of an example de-escalatingmodification (suppression) process on the MGSs content of example scorevector 502 of FIG. 5C. FIG. 5D shows the original (unmodified, orunsuppressed) set of MGSs that are used in the selection of the firstimage. (The first image is selected using the original MGS values.) FIG.5E shows modified (suppressed) MGSs that are used in the selection ofthe second image. (The second image is selected after the (original) MGSvalues are modified, or suppressed, with respect to the first selectedimage.) FIG. 5F shows modified (suppressed) MGSs that are used in theselection of the third image. (The third image is selected after the(original) MGS values are modified, or suppressed, with respect to thefirst and second selected images, using the minimum distance, Dmin.,identified between the third image and the nearest selected image.)

Referring to FIG. 5D, the (original) score value 90 is currently thegreatest MGS (e.g., MGS=90=MGS|max) in score vector 502. Therefore, theimage resulting in this score value (the image related to this MGS) isselected. After the image is selected, this score value is zeroed out toensure that the selected image will not be selected again. FIG. 5Eshows, at 510, the greatest score value 90 zeroed out. FIG. 5E alsoshows the other MGSs in score vector 502 which are modified version ofthe original MGSs shown in FIG. 5D.

As described herein, the score value modification (suppression) processvariably modifies the score values (MGSs) of images in a de-escalatingway. By way of example, score value 80 in vector cell No. 1 in FIG. 5Dis adjacent to score value 90 in cell No. 2, but score value 78 invector cell No. 6 in FIG. 5D is the farthest from score value 90 in cellNo. 2. Assuming that MGS=80 is related to an image that is much closerto the selected image (e.g., to the image related to MGS=90) than theimage related to MGS=78, MGS=80 in vector cell No. 1 (FIG. 5D) isrigorously reduced (the reduced value is shown in FIG. 5E, cell 1) by arelatively large number, for example from 80 to 65. However, the MGS=78(in vector cell No. 6 (FIG. 5D)), which is related to the remote image,is leniently (moderately, or somewhat) reduced (the reduced value isshown in FIG. 5E, cell 6) by a relatively small number, for example from78 to 76. The rest of the MGSs may be modified (reduced) using the sameprinciple.

After the first MGSs modification process is performed, the next (inthis case the second) image may be selected based on the modified scorevalues. Referring to FIG. 5E, among the modified score values (among themodified MGSs) the score value ‘76’ is the greatest (MGS=76=MGS|max), sothe image resulting in this score value may be selected, and, after thesecond image is selected, the score value related to it (score value 76)may be zeroed out, or otherwise manipulated, to ensure that the secondimage will not be selected again. FIG. 5F shows, at 520, the greatestscore value 76 zeroed out. FIG. 5F also shows the other MGSs in scorevector 502 which are modified version of the original MGSs shown in FIG.5D.

Every original MGS in FIG. 5D may be modified differently from one imageselection to another, because an original MGS of an unselected image ismodified based on Dmin., which is a distance between its related imageand the nearest selected image, and the distances between unselectedimages and already selected images may change as more and more imagesare selected. After the second MGSs modification process is performed,the next (at this stage the third) image may be selected based on the(new) modified score values. Referring to FIG. 5F, among the modifiedscore values the score value 68 (cell No. 4, FIG. 5F) is the greatest(MGS=68=MGS|max image resulting in this score value may be selected. Ifthe image selection budget includes only three images, the imagesselection process may be terminated at this stage. However, if the imagebudget is larger and the score vector includes more (say, hundreds orthousands of) MGSs that are respectively related to as many images, theimages selection process may continue in a similar way until the imagebudget is used up.

The image selection methods disclosed herein (for example in FIGS.6A-6B, and in FIG. 7 ) in some embodiments guarantee that: (1) the totalnumber of selected images does not exceed a predetermined number ofimages (e.g., an image budget), (2) the selected images include the mostclinically important pathologies, and (3) the image selection processesdisclosed herein can be ‘fine-tuned’ to select (e.g., for analysis orfor displaying): (3.1) only images that were captured from a particularsegment or portion of the GI system, and/or (3.2) images that include aparticular type of pathology (e.g, polyps).

The particular segment or portion of the GI system may be relativelylong (e.g., small bowel), or relatively short (e.g., colon). The imageselection process may be fine-tuned to select only images that weretaken from a particular segment of the small bowel, or from a particularsegment of the colon, or from any other GI segment of interest.Fine-tuning the image selection process to selectively select imagesthat were taken from a particular GI section may be performed by, forexample, zeroing, out the extraneous score values of all the images thatwere taken outside the GI segment of interest. (An example of suchselective image selection process is shown in FIG. 8 , which isdescribed below.)

FIG. 6A shows a method of selecting images for display (or for any otherpurpose) according to an example embodiment of the invention. FIG. 6A isdescribed in association with FIG. 1 and FIG. 3 . The image selectionmethod of FIG. 6A is applicable to a group of images that is obtained invivo from one imager (e.g., Imager-1, FIG. 2 ) of an in-vivo device. Theembodiment shown in FIG. 6A includes an image selection iteration orrepetition loop (690) that selects images (for example for furtherprocessing, analysis and/or for display), one image periteration/repetition.

At step 610, data processor 14 may receive a group of contiguous images(e.g., group 300) contiguously captured in-vivo by in-vivo device 40 ina body lumen. Data processor 14 may calculate, at step 610 or in aseparate step, for each image in the image group, a general score (GS)that may indicate a probability that the image includes at least onetype of pathology. Data processor 14 may receive a GS value for eachimage with the image, or it may calculate it ‘in-situ’ as a function ofscores that the pathology detectors D1, . . . , Dm (pathology detectors15, FIG. 1 ) may output for the images. Alternatively, data processor 14may not need the images but, instead, obtain only the GSs related to theimages and, in addition, data processor 14 may obtain informationregarding the relative location of each image in the image group.

At step 620, data processor 14 may partition the image group into imagesubgroups such that each image subgroup includes a base image (e.g.,base images 330, 340 and 350 in FIG. 3 ) and subsequent images thatresemble or are similar to the base image. (Partitioning an image groupmay be performed, for example, in the way described in connection withFIG. 3 .)

As each image subgroup may include many images, where with each image isassociated a general score, data processor 14 identifies, at step 630,in each image subgroup the general score having the highest value. (Thegeneral score having the highest value in an image subgroup is referredto herein as the maximum general score (MGS) of the image subgroup.)

At step 640, data processor 14 identifies MGS|_(max), which is the MGShaving the highest value among all the MGSs of the image subgroups, and,at step 650, data processor 14 may select the image that is related toMGS|_(max), for example for further processing (e.g., analysis) and/orfor display, and assign the value one to a loop counter n (at step 660).

At step 670 data processor 14 checks whether the number of alreadyselected images is N, which is the number of images permitted forselection by a predetermined image budget. If the image budget hasalready been used up, that is, if n≥N (this condition is shown as “Yes”at step 670), processor 14 may terminate the image selection processand, for example, analyze only the selected images, or display theselected images, for example, in a separate display window. However, ifthe image budget has not been used up, that is, if n<N (this conditionis shown as “No” at step 670), processor 14 may nullify, at step 680,the value of the MGS|_(max) of (e.g. associated with, or calculated for)the selected image, to ensure that the currently selected image will notbe selected again, thus giving a chance to other images to be selected.(‘Nullify’, in the context of the invention, means setting a value of aMGS|_(max) to a value; e.g., to zero or to a negative value, thatreduces to zero the probability that the related image would be selectedagain.) It is noted that a score threshold may be used instead of, or incombination with, the predetermined number N of images, as described,for example, in connection with step 616 of FIG. 6B, which is describedbelow.

At step 682, processor 14 may modify the (original) MGSs of the imagesubgroups such that the value of each particular (original) MGS changes(for example, by reducing its value by processor 14) based on a distancethat exists between the image that is related to the particular originalMGS and a nearest already selected image. For example, the closer acandidate image is to the nearest selected image, the more the value ofthe MGS related to the candidate image is reduced. (A ‘candidate image’,as used herein, is any image in an image subgroup, whose general scorehas been identified, for example by data processor 14, to be an MGS, andthe image has not been selected yet, though it may be selected in someother repetition 690, or not selected at all, hence the adjective‘candidate’ in ‘candidate image’.) For example, as at this stage onlyone image has been selected (n=1), each MGS is modified depending on thedistance between its related (candidate) image and the one selectedimage, and the modification ‘factor’, or process, de-escalates withdistance from the selected image. (Throughout the specification,modification of an MGS means modification of an original MGS, and a‘modified’ MGS is an MGS having a value which is reduced, or lowered,relative to a corresponding original MGS. An original MGS and a modifiedversion of the original MGS are both related to a same image.)

At step 684 (after the MGSs are modified), data processor 14 mayidentify a modified MGS that currently has the greatest value (that is,MGS|max) among the modified MGSs. At step 686, data processor 14 mayselect the image related to the new identified MGS|max. At step 688,data processor 14 may increment the loop counter, n, by one, in eachrepetition, until all the image budget is used (that is, until n=N,N=100).

If at step 670 the value of the loop counter, n, is still less than N,data processor 14 may nullify, at step 680, the MGS|max that is relatedto the last image that was selected, then modify, at step 682 each MGSbased on the distance between the image related to the MGS and thenearest selected image. For example, if two images ‘A’ and ‘B’ (e.g.,out of an image budget of, for example, ten images) have already beenselected, then every particular MGS is modified according to a distance,d, between the image related to the particular MGS and the nearestselected image. For example, if the image related to the particular MGSis closer to, say, image ‘A’, then the distance, d, between the imagerelated to the particular MGS and image ‘A’ is used to modify theparticular MGS, and, as described herein, the shorter d, the greater thereduction in the value of the particular MGS. Every time data processor14 performs loop 690 to select an image, the (original) MGSs aremodified in a different way because the distances between candidateimages and selected images, which are the basis for the MGSsmodification process, change every time another image is selected.

A variant of the embodiment shown in FIG. 6A may include steps orderedas shown in FIG. 6A, and the images selection process may include: (i)identifying, according to step 640 (or step 684, depending on the stageof the process), the greatest MGS (MGS|max) in the MGSs identified forthe image subgroups and, according to step 650 (or step 686, dependingon the stage of the process), selecting the image related to thegreatest MGS; (ii) nullifying, according to step 680, the MGS identifiedat step 640 (or at step 684) related to the selected image; (iii)modifying, according to step 682, each MGS (original MGSs based on adistance d between an image related to the particular MGS and eachselected image; (iv) identifying, according to step 684, in the modifiedMGSs, a modified MGS which has the maximum value (e.g., MGS|max). Anembodiment of the method may further include selecting, according tostep 686, the image that is related to that modified MGS|max, andrepeating steps (ii)-(iv) until a predetermined criterion selected froma group consisting of a number N of images and a score threshold is met.

FIG. 6B shows an example implementation of the image selection method ofFIG. 6A. FIG. 6B is described in association with FIG. 1 and FIG. 6A.‘Score’ is a score vector initially including original MGSs for multipleimage subgroups (e.g., one MGS per image subgroup). Vector ‘Score’ mayalso include modified MGSs during the image selection process.

‘ScoreOrig’ is a score vector holding the original MGSs for each imageselection repetition loop repetition loops 690 and 632). The entirecontent of the Score vector may change as a result of the modificationof the MGSs every time an image is selected. The content of theScoreOrig vector, on the other hand, changes one MGS at a time due tothe nullification of one MGS every time an image related to thisparticular MGS is selected. (In every repetition, another MGS, which iscurrently identified as MGS|max, is nullified, until N MGSs in thevector ScoreOrig are nullified.)

At step 612, data processor 14 initializes vector ScoreOrig with theoriginal MGSs that are initially stored/contained in vector Score. Atstep 612 data processor 14 may also reset a loop counter, n, to, orinitializes it with, a reference number, for example zero.

At step 614, data processor 14 checks if a number N of images have beenselected (it checks whether the image budget has been used up). If Nimages have already been selected (the condition is shown as “Yes” atstep 614), data processor 14 may terminate the image selection process.However, if less than N images have been selected (the condition isshown as “No” at step 614), data processor 14 may, at step 616,identify, in the Score vector, the MGS currently having the maximumvalue (that is, MGS|max). If a MGS|max has been found but its value islower than a predetermined score threshold, Sth, (e.g., if MGS|max isequal to zero, or if it is negative), the condition is shown as “No” atstep 616, data processor 14 may terminate the images selection process.However, if the identified MGS|max is greater than the score thresholdSth (the condition is shown as “Yes” at step 616), data processor 14 mayselect, at step 618, the image related to the MGS currently identifiedas the MGS|max. In some embodiments, iteration/repetition loop 63:2 maynot include step 614 (e.g., this step may be skipped, removed orignored), and the number of images ultimately selected may depend on thevalue of Sth. (The lower the Sth, the greater the number of selectedimages.) Therefore, by using only the score threshold, Sth, or usingboth the image count limit N (at steps 614) and the score threshold Sth(at step 616), the number of images ultimately selected by the processmay be known only post factum; that is, only after the image selectionprocess is completed. However, the user may change, or set, the value ofthe score threshold to reduce or increase the number of images to beselected, and the user may also limit the number of images to a desirednumber by setting a corresponding value to the parameter N.

At step 622, data processor 14 may nullify, in the vector ScoreOrig, theMGS that it currently identifies as the MGS|max, for example byreplacing the value MGS|max in vector ScoreOrig with, for example, thevalue (−∞), or with the value zero, in order to exclude this image fromthe image selection process (to ensure that this image will not beselected more than once). Then (still at step 622, or in a separatestep), data processor 14 retrieves MGSs from the vector ScoreOrig, andmodifies the retrieved MGSs by using a modification function f(d).Finally, data processor 14 stores the modified MGSs in the Score vector,thus updating the content of the Score vector with new modified MGSsvalues every time a new image is selected at step 618.

If the allocated image budget has not been used up (per step 614), thenext greatest MGS (MGS|max) among the modified MGSs is identified, atstep 616, and the image related to MGS|max (among the modified MGSs) isselected, at step 618, and so on.

Modifying Original MGSs and GSs

The modification function f(d) factors in distances (e.g., in terms ofnumber of images that intervene, or are temporally interposed) betweenan image just selected, for display and candidate images in the imagegroup. In some embodiments, modification function f(d) may be devisedsuch that the closer a candidate image is to a currently selected image,the greater is the function's output value, and, therefore, the lowerthe value of the MGS subject of the modification process. For example,assume that images are numbered in the order of their capture, and anexample image number 250 has just been selected for display, Continuingthe example, an example image number 290 in the group of images is 40images away from image number 250 (d=290−250=40 images), and an exampleimage number 100 in the series of images is 150 images away from imagenumber 250 (d=|100−250|=150 images). The function f(d) may be selected(configured) such that the score value of the closer image (in thisexample the score value of image number 290) would be rigorouslysuppressed (substantially lowered) by the function f(d) outputting, forimage 290, a relatively high number (for example 45). In contrast, thescore value of example image 100 would be only leniently (lessrigorously, or somewhat) ‘suppressed’ by function f(d), for example bythe function f(d) outputting, for image 100, a relatively low number(for example 12). (As shown in FIG. 6B, a modified score may becalculated, for example, by using the formula Score=ScorOrig-f(d), cf.step 622. Therefore, the greater the value of f(d), the smaller thevalue of Score, and, therefore, the greater the modification of theoriginal score.)

The rationale behind the distance-dependent MGSs modification process isthat if a candidate image near an already selected frame is clinicallyvery important (which is indicated or inferred by its relatively highMGS value), there would still be a good chance for the near image, evenafter its MGS value is made smaller rigorously, to be selected in a nextiteration or repetition. However, if the candidate image near theselected image is of low clinical importance, which may be reflected inits relatively low score value, or if it is as clinically important asthe selected image, it may be beneficial to ‘skip’ it (skip the nearcandidate image by reducing its chance to be selected) by lowering itsscore value so that a, candidate image that is relatively far from theselected image may have a better chance to be selected even if the farcandidate image and the near candidate image may be similar in terms ofclinical importance. Put differently, it may be beneficial to select fordisplay an image that is near a selected image only if the original MGSvalue of the near image is relatively very high, which indicates orinfers that the near image is clinically very important, so thatlowering, even significantly, the high score value of the near imagewould, in such a case, still give that image a chance to be selected inthe next image selection iteration/repetition. Another rational is thatthe closer an image is to an already selected image, the greater is theprobability that the selected image and the image near the selectedimage show or include a same pathology, so it may be beneficial toultimately select only one image of the two in favor of images that mayshow other pathologies somewhere else in the body lumen.

It may be that a selected image is selected because its related polypscore has a high value, and an image near the selected image has highscore value because of bleeding. In such a case, in order to ensure thatboth images (which are related to different pathologies) are selected,the score's suppression process may be applied to scores selectively;that is, it may be applied only to scores that are related to a sametype of pathology (e.g., to polyps, or to bleeding, etc.).

The modification function f(d), may be any function that reduces thevalue of an MGS as a function of the distance, d, between an imagerelated to the MGS and a nearest image that was already (previously)selected. The modification function, f(d), may be devised such that theshorter the distance, d, between the two instances (the image related toa MGS and the selected image which is the nearest to that image), thegreater (the more rigorous) the reduction in the value of the MGS. Thefunction, f(d), may be linear (e.g., f(d)=a*d, where ‘a’ is acoefficient), or non linear (e.g., f(d)=a*d²+b*d+c, where ‘a’ and ‘b’are coefficients and ‘c’ is a constant). The function, f(d), may beGaussian exponent, or exponent with an absolute value of distance, or astep function, or triangularly shaped with its apex, or peak, located ata selected frame/image and linearly decreases with distance.

The distance, d, between images (for example between an image related toan MGS and a selected image) may be calculated or measured in units of:(i) time, or (ii) a number of intervening images, or (iii) a number ofintervening image subgroups, or (iv) a distance that the in-vivo devicetravelled in the body lumen while taking the images, or (v) a distancethat the in-vivo device travelled in the body lumen with respect to amarked landmark in the group of images, corresponding to a landmark inthe body lumen, or (vi) percentage of a video clip including all, ormost of, the group of contiguous images, or (vii) estimated distance(e.g., via linear progress or similar techniques).

Each of the unit instances described above may be calculated or measuredas: (i) an absolute value, for example as a value that may be calculatedor measured relative to a beginning of the group of contiguous images,or relative to a beginning of the body lumen, or relative to anylandmark in the group of contiguous images or in the body lumen, or (ii)a percentage, for example as a percentage of the entire group ofcontiguous images, or as a percentage of the body lumen or portion ofthe body lumen, or (iii) a percentage of a video clip including all, ormost of, the group of contiguous images, or (iv) a percentage of adistance travelled by the in-vivo device, or (v) a percentage of a timetravelled by the in-vivo device, for example a percentage of time ittakes the in-vivo device to traverse a distance captured in a certainseries of images). The body lumen may be the GI tract or an organ of theGI system.

Data processor 14 may enable a user (e.g., a physician) to select amodification function according to the part of the body lumen from whichimages were taken, and this may be beneficial because, by selecting asuitable modification function, the user may bias the image selectionprocess towards (to ‘favor’, or to give preference to) a particular partof the body lumen the user is more interested in by, for example,leniently (somewhat) reducing the values of the MGSs related to(originating from) the focused on lumen part, while rigorously reducingthe values of the MGSs originating from other lumen parts.

FIG. 6C demonstrates an example group 652 of contiguous images accordingto an example embodiment. FIG. 6C is described in association with FIG.6B. Assume that image group 652 includes a large number of (for example100,000) contiguous images. The large number of images may be normalizedsuch that each image has a normalized image number. For example, theimage captured first (the image shown at 642) may be assigned the value0.00, and the image captured last (the image is shown at 644) may beassigned the value 1.00. Also assume that a score vector similar to thevector Score of FIG. 6B includes eight MGSs, designates, in FIG. 6C, asMGS₁, MGS₂, . . . , MGS₈, that respectively correspond to eight images,and that the number of images to select (for example for furtheranalysis and/or for display) is three, that is, assume that N=3).

Assume that, per step 616 of FIG. 6B, the value of MGS₆ is currently thehighest among MGS₁, MGS₂, . . . , MGS₅ (e.g., MGS₆=MGS|max), and,therefore, assume that, per step 618, the image related to MGS₆ is thefirst image that is selected. At step 622, the value of MGS₆ isnullified (in the related score vector ScoreOrig.), and each MGS_(i) ofMGS₁, MGS₂, . . . , MGS₈ is modified based on the distance, d, betweenthe image related to the MGS; and a nearest selected image. (At thisstage only one image (image 0.63 corresponding to MGS₆) was selected, soall MGSs are modified with respect with this image.) For example, thedistance d1 between the image related to MGS₁ and the selected imagerelated to MGS₆ is 0.61 (d1=0.63−0.02=0.61). Similarly, the distance d2between the image related to MGS₅ and the selected image related to MGS₆is 1.016 (d2=0.63−0.47=0.16). Similarly, the distance d3 between theimage related to MGS₇ and the selected image related to MGS₆ is 0.11(d3=0.74−0.63=0.11), the distance d4 between the image related to MGS₃and the selected image related to MGS₆ is 0.45 (d4=0.63−0.18=0.45), andso on. (The distances of the other MGSs may be found in the same way.)Each MGS is, then, modified according to the distance of its relatedimage from the selected image. If the images budget has not yet beenused up (per step 614, FIG. 6B), then after all MGSs are modified, perstep 622, to result in modified values MGS′₁, MGS′₂, MGS′₃, MGS′₄,MGS′₅, MGS′₆ (MGS₆ is nullified), MGS′₇, MGS′₈, a maximum MGS (that is,MGS|max) is identified, at step 616, among the modified MGSs, and theimage (at this stage the second image) related to the identified MGS|maxamong the modified MGSs is selected at step 618. Assume that themodified MGS currently having the greatest value is MGS′₂ (that is,MGS′₂=MGS|max) and, therefore, the second selected image is the imagerelated to MGS′₂.

After the second image (the image related to MGS′₂ and MGS₂) is selected(at step 618), the original MGS₂ is nullified (in the score vectorScoreOrig, at step 622), and the distance between each image that isrelated, to each of the other (the non-nullified) MGSs and each selectedimages is calculated in order to identify, for each candidate image, thenearest selected image. Then, each original MGS is modified again, thistime according to a distance between the particular image related toeach modified MGS and the selected image that is the nearest to theparticular image. In other words, as more images are selected using theimage selection process, the distances, based on which the MGSs aremodified, may change. For example, after image (normalized) number 0.63is selected as the first image (because its MGS₆ is the greatest), imagenumber 0.28 is at distance d4 from image number 0.63, so MGS₃ (the MGSof image number 0.28) is modified according to distance d4. However,after image number 0.18, corresponding to MGS₂, is selected as thesecond image (because MGS′₂ is the new greatest MGS in the secondselection repetition), the distance d5 between image number 0.28 andimage number 0.18 is shorter than the distance d1 between image number0.28 and image number 0.63. (Distance d5=0.10, distance d4=0.35.)Therefore, the value of original MGS₃ is modified, after the secondimage is selected, according to the distance d5, which is the distancefrom image number 0.28 to the nearest selected image. Similarly, afterthe second image (linage number 0.18) is selected, image number 0.02 iscloser to selected image 0.18 than to selected image 0.63 (d6<d1).Therefore, the original MGS₁ related to image number 0.02 is modified,after the selection of the second image, using distance d6. (Afterselection of the first image the original MGS₁ is modified usingdistance d1.)

In one embodiment the same distance calculation process applies to allother MGSs and every time a new image is selected. (As described herein,distances between candidate images may be calculated or measured inunits of time (e.g., capture time of images being compared), number ofintervening images, number of image subgroups, and, in fact, anysuitable technique.)

Referring to FIGS. 6A-6B, formula (4) is an example scores (MGSs)modification function, and formula (5) is an example formula showing anexample way of using f(d) to modify the MGSs:f(d=Dmin(i))=(−30)*exp.{−(1,000*Dmin(i)²}  (4)Score(i)=ScoreOrig(i)−30*exp.{−(1,000*Dmin(i))²}  (5)where ScoreOrig(i) (i=1, 2, 3, . . . ,) is an original MGS(i) value inscore vector ScoreOrig, Dmin(i) is a distance, d, between an imagerelated to MGS(i) and the nearest selected image, and Score(i) is a newscore resulting from the modification of the corresponding (original)MGS. (Dmin(i) may have a value, for example, in the range of [0.0,1.0],which is in accordance with FIG. 6C.) As described herein, after two ormore images are selected, a candidate image may be closest to oneparticular selected image, and Dmin(i) designates the distance betweenthe candidate image and the closest selected image. Referring to formula(5), the shorter is d (the smaller the value of Dmin(i)), the greaterthe value that is detracted or removed from ScoreOrig(i). (The morerigorously the related MGS is modified.)

While some method(s) described herein, for example in connection withFIGS. 6A-6B, are applicable to an in-vivo device including one imager,embodiments may be generalized to an in-vivo device that includes anynumber, q, of imagers, as described below. Briefly, while an in-vivodevice with one imager produces one image group that produces oneMGS|max value at a time (e.g., in each image selection repetition 690,FIG. 6A), an in-vivo device with many imagers produces as many imagegroups that produce as many MGS|max values at a time, and, each time(e.g., during each image selection repetition 770, FIG. 7 ), thegreatest MGS|max among all the MGS|max values is identified, and theimage related to the greatest MGS|max is selected. By way of example, amethod for selecting images from a number q of groups of contiguousimages may include performing, for q groups of contiguous imagesrespectively captured in a body lumen by q imagers, where each image ofeach group may be associated to a general score (GS) indicative of aprobability that the image includes at least one type of pathology, andto a distance, d, indicative of a distance between images of therespective group, may include, for each image group of the q groups ofcontiguous images: (A) dividing, or separating, the image group, or aportion thereof, into image subgroups, each image subgroup including abase image and images resembling to the base image, and (B) identifyinga set Set(i) (i=1, 2, 3, . . . ) of maximum general scores (MGSs), theset Set(i) maximum general scores including a maximum general score(MGS) for each image subgroup of the image group, and selecting imagesfor processing, wherein the image selection may include: (i) identifyinga maximum MGS (MGS|max) in each set S(i) of MGSs; (ii) identifying,among all the MGSs|max, the greatest MGS|max, and selecting the imagerelated to the greatest MG S|max; (iii) nullifying the greatest MGS|maxrelated to the selected image; (iv) modifying the particular set Set(i)of MGSs related to the selected image based on a distance d betweenimages respectively related to the MGSs of the particular set Set(i) ofMGSs and each selected image; and repeating steps (i)-(iv) until apredetermined criterion selected from a group consisting of a maximumnumber, N, of images and a score threshold, Sth, is met. An examplemethod for a case where the number q of imagers, and thus of groups ofcontiguous images, is equal to two, is described in connection with FIG.7 , which is described below.

FIG. 7 shows a method for selecting images for display according toanother embodiment of the invention. FIG. ‘7 is described in associationwith FIG. 1 , The images’ selection method of FIG. 7 is applied to twoimage groups respectively obtained in vivo from two imagers (e.g.,imager-1 and Imager-2, FIG. 2 ) of an in-vivo device. (FIG. 7 refers toa system configuration where the in-vivo device includes two imagers.)The embodiment shown in FIG. 7 may include an iteration or repetitionloop (770) to select images for display (or for other purposes; e.g.,for further analysis), one image, per iteration/repetition. Eachiteration/repetition may result in the selection of an image thatoriginates from one of the two imagers. One imager may ultimatelyprovide more (selected) images than the other imager, and the images'selection method in some embodiments guarantees that nu the total numberof images selected from both imagers does not exceed the allocatedimages budget. As described in connection with step 616 of FIG. 6B,using an ‘image budget’ or limit may include completing the imageselection process when the number of selected images is equal to apredetermined number, N, of images, or it may include using a scorethreshold, Sth, or it may include using both predetermined number, N, ofimages, and a score threshold in combination. Using a score threshold(Sth), in some embodiments, guarantees selection of some finite (thoughunknown in advance) number of images because the values of the MGSsdecrease from one image selection loop to another, so that, at somepoint, a remaining maximum MGS would necessarily be, after a finitenumber of iterations 770, smaller than the score threshold.

After images originating from the two imagers are pathology-wiseprocessed (e.g., by data processor 14, or by a remote computer) in theway described herein, each image group is independently partitioned intoimage subgroups. Data processor 14 may identify one or more local maximascores, MGSs, in each image subgroup of each image group, and dataprocessor 14 may create two score vectors for separately accommodatingthe two sets of MGSs: (1) a score vector Score(1) for accommodating theoriginal MGSs originating from the image group (image subgroups)associated with the first imager, and (2) a score vector Score(2) foraccommodating the original MGSs originating from the image group (imagesubgroups) associated with the second imager. Each score vector Score(i)(where j=1, 2) may include a plurality of local maxima score values,MGSs. In some embodiments, every local maxima score value, MGS, in everyimage subgroup (in both image groups) is stored in the respective vectorScore(j). (The index ‘i’ as used herein, indicates single vector value;Score(i) is an individual score value. The index ‘j’ indicates a wholevector; e.g., Score(j) is a vector including multiple score values.) Inother embodiments, only the greatest local maxima score value, MGS, ineach image subgroup is stored in the respective vector Score(j). Inother embodiments, the greatest score value in each image subgroup isselected for the respective vector Score(j) regardless of whether it is,or it is not, a local maxima score value.

At step 710, data processor 14 may initialize a score vectorScoreOrig(1) associated with a first imager with the (original) MGSsvalues that are initially stored in score vector Score(1), and,similarly, it may also initialize a score vector ScoreOrig(2) associatedwith a second imager with the (original) MGSs values that are initiallystored in vector Score(2). The MGSs initially stored in each of scorevector ScoreOrig(1) and score vector ScoreOrig(2) are referred to hereinas ‘original MGSs’ Modified MGSs are stored in score vector Score(1) andin score vector Score(2). (The (original) MGSs values stored in scorevector Score(1) change every time an image is selected from images whichare related to MGSs that are stored in this vector. Similarly, the(original) MGSs values stored in score vector Score(2) change every timean image is selected from images which are related to MGSs that arestored in this vector.) At step 710 data processor 14 may also reset aloop counter, n, to an initial value; e.g., to one.

At step 720 data processor 14 may check if a number of N images havebeen selected (the processor may check whether the image budget has beenused up). If N images have already been selected (the condition is shownas “Yes” at step 720), data processor 14 may terminate the imageselection process. However, if less than N images have been selected(the condition is shown as “No” at step 720), data processor 14 may, atstep 730, identify iii each score vector Score(j) the MGS currentlyhaving the maximum value (this MGS is referred to herein as ‘MGS|max’).Namely, data processor 14 may identify MGS|max(1) in score Score(1), andMGS|max(2) in score Score(2). Then, data processor 14 may select fromMGS|max(1) and MGS|max(2) the MGS|max(i) having the highest value. IfMGS|max(1) is equal to or greater than MGS|max(2) (this condition isshown as “Yes” at step 730; j=1), the MGSs modification processperformed at step 760 will be applied to the original MGSs associatedwith the first imager (to the original MGSs stored in score vectorScoreOrig(1)), and if MGS|max(2) is greater than MGS|max(1) (thiscondition is shown as “No” at step 730; j=2), the MGSs modificationprocess will be applied to the original MGSs associated with the secondimager (the original MGSs stored in score vector ScoreOrig(2)).

At step 740, if the value of the selected MGS|max(i), where i=1 or i=2(depending on which MGS|max(i) is greater: MGS|max(1) or MGS|max(2)), isequal to zero, or it is negative, or, in a general case if it is lowerthan a predetermined positive score value threshold, (the condition isshown as “No” at step 740), data processor 14 may terminate the imagesselection process. (Depending on the value of the score threshold, itmay occur that the image selection process is terminated before N imagesare selected.) However, if the selected MGS|max(i) is greater than zero(the condition is shown as “Yes” at step 740), or if it is greater thanthe score threshold, data processor 14 may select, at step 750, theimage related to the MGS|max(i) currently identified as the greatestMGS|max among the current MGS|max(1) and MGS|max(2). (The values ofMGS|max(1) and MGS|max(2) may change from one repetition loop 770 toanother.)

At step 760, data processor 14 may nullify, in the score vectorScoreOrig(j) the MGS that it currently identified as the greatestMGS|max(i) in the two score vectors Score(1) and Score(2), in order toexclude the selected image from the image selection process (to ensurethat this image will not be selected more than once). Data processor 14may nullify the MGS by, for example, replacing the value MGS|max(i) inthe associated score vector ScoreOrig(j) with the value (−∞), or withthe value zero. Then (still at step 760, or in a separate step), dataprocessor 14 may retrieve the original MGSs from the related scorevector ScoreOrig(j) (the score vector ScoreOrig(j) associated with theMGS|max(i) that was selected at step 730), then modify the retrievedMGSs by using a modification function f(d), and store the modified MGSsin the respective score vector Score(j), thus updating the scorescontent of score vector Score(j) with new modified MGSs values everytime a new image is selected at step 750 from the corresponding imager.

When step 730 is performed for the first time, both score vectorsScore(1) and Score(2) contain (store) original MGSs. Therefore, firstexecution of step 730 involves identifying, and comparing between, anoriginal MGS|max(i) in the score vector Score(1) and an originalMGS|max(i) in the score vector Score(2). When the first image isselected at step 750, the related score vector Score(j) in which therelated MGS|max was identified is modified, which process results inmodified MGSs in score vector Score(j). Therefore, the second executionof step 730 involves comparing between an original MGS|max stored in theother, yet unmodified, score vector Score(j) and a modified MGS|maxfront the modified score vector Score(j). However, assuming that at acertain point in the image selection process at least one image has beenselected from each image group, further executions of step 730 includescomparing between modified MGS|max values, though in each repetitionloop (770) a different score vector Score(j) is modified, and eachmodification of score vector Score(j) is done with respect to therespective original MGSs.

The image selection methods described herein may be similarly applied toany number q of imagers respectively capturing q groups of images,including to one imager. For a general case where there are q imagegroups, each group of images may result in one or more image subgroupand related original MGSs, and, in addition, a number q of MGS|maxvalues (an MGS|max value per image group) may be compared. (at a stepsimilar to step 730) in each image selection iteration or repetitionloop in order to select, in each image selection iteration loop, oneimage that is related to the MGS|max having the greatest value among allthe q MGS|max values.

FIG. 8 schematically illustrates an implementation of an image selectionmethod according to an example embodiment. A human GI tract(schematically shown at 800) includes, among other things, a small bowel(SB) segment 810, and a colon 820 segment, An in-vivo device similar toin-vivo device 40 may capture images in GI tract 800 while moving indirection 830.

Graph 850 is a time graph showing temporally ordered images inassociation with the part of GI tract from which the images werecaptured. For example, the first image that was captured in the SB (810)is image 812, which was captured at time t1, and the last image capturedin the SB (810) is image 814, and it was captured at time t2 (t2>t1).Similarly, the first image that was captured in the colon (810) is image822, which was captured at time t3, and the last image captured in thecolon (810) is image 824, and it was captured at time t4 (t4>t3>t2>t1).According to graph 850 images were captured using a constant imagingrate. However, the imaging rate may vary, for example according tochanges in the movement of the in-vivo device, or according to changesin the remaining battery power, etc., and the image selection methodsdisclosed herein may, as well, apply to embodiments where the imagingrate varies.

Assuming that the user (e.g., a physician) wants to select only imagesthat originate from a particular segment of GI tract 800, the user mayindicate this to a workstation (e.g., workstation 13), for example bymouse-clicking a dedicated input box on a computer display (e.g.,display 18), and a data processor (e.g., data processor 14), complyingwith the user's input, may exclude all extraneous parts of the GI tract(800) from consideration by zeroing out all general scores that arerelated to the extraneous GI parts. For example (referring to graph860), it is assumed that the user inputs to the workstation (for thedata processor) an instruction to select only those ‘pathological’images that originate from the small bowel (810). In response to theuser's instruction, the data processor may zero out the general scores(GSs) related to the images that precede the images of the small bowel,as shown at 862 (image numbers 123-124), and also the general scoresrelated to the images that follow the images of the small bowel, asshown at 864 (image numbers 150-163). By zeroing out (e.g., set zero)the general scores related to the images to be excluded, the imagesultimately selected by the data processor originate only from the smallbowel. (‘Preceding a particular image’ and ‘following a particularimage’ respectively mean an image captured before the particular image,and an image captured after the particular image.)

An image may be noisy, for example, in the sense that it containsbubbles and/or gastric content and/or if the image provides tissuecoverage below a tissue coverage threshold value and/or if the imagecomplies with a darkness criterion, etc. In such cases, it may bebeneficial to exclude such images from the image selection process. Tothat effect, the image selection process limy additionally include, forexample, a bubble detector and/or a gastric content detector and/or atissue coverage detector, etc., to detect such images, and the methodsdescribed herein may include zeroing out (e.g., setting to zero) ageneral score of an image if the image is noisy. By way of example,image numbers 132 and 139 are example images containing bubbles andgastric content, so the general scores related to them are respectivelyshown zeroed out at 866 and 868.

In another example (referring to graph 870), it is assumed that the userinputs to the workstation (for the data processor) an instruction toselect only images that originate from the colon (820). In response tothe user's instruction, the data processor may zero out the generalscores related to the images preceding the images of the colon, as shownat 872 (image numbers 123-149), and also the general scores related tothe images that follow the images of the small bowel, as shown at 874(image numbers 162-163). By zeroing out the general scores related tothe images that are to be excluded from the image selection process, theimages ultimately selected by the data processor originate only from thecolon.

After the images related to the extraneous segments of GI tract 800 areexcluded from the image selection process (e.g., by zeroing out theirgeneral scores), the non-excluded images (the remaining images that arecaptured in the GI's segment of interest) may be partitioned into imagesubgroups, then, in each image subgroup an MGS (or MGSs) may beidentified, and, then, the MGSs of the image subgroups may be processedin order to select images from the GI segment of interest (e.g., smallbowel segment 810, or colon 820, or any other segment of GI tract 800).

In some embodiments that are described herein, for example in connectionwith FIG. 6A, the GSs of images that are near a selected image arevariably suppressed (e.g., their value is lowered), thus still givingthe near images a chance to be selected as well. Typically, the numberof images that are near a selected image and contain the same object isknown only post factum, that is, after the tracking process, whichsearches for a certain object in the near images, is completed for therelated selected image. (A tracking process may be regarded as completedafter the last image containing a same object as in the selected imageis found by the processor performing the tracking process. (The objectcontained in a selected image is referred to herein as ‘originalobject’.) That is, the processor may in an orderly manner ‘pervade’ thetracking process from a selected image to one image after another (inboth directions) as long as it finds the original object in theseimages, and it may terminate the tracking process when a next image doesnot contains the original object.) In addition, if many images on oneside (within the ordering in the image stream) or on two sides of aselected image contain the original object, such images maybe relativelychronologically remote from the selected image (e.g., 50 images awayfrom the selected image, 45 images ahead of the selected image, etc.)and such images may still be regarded as ‘near’ images. (A remote imagemay still be regarded as a near image if the tracking process indicatesthat the remote image, and all the images intervening between the remoteimage and the selected image, all contain the same object.) Using thisembodiment may, therefore, result in redundant selection of additionalimage(s) that contain (show) a same object (e.g., pathology), forexample a same polyp, a same lesion, etc. However, in some cases it maybe desired to ensure that only one image that shows a particular object(e.g., pathology) be selected in order to avoid selecting redundantimages and, in general, to better use a given (e.g., predetermined)image budget. Using the embodiment shown in FIG. 9 may ensure that onlyone image is selected for each object (e.g., for each particularpathology). After a particular image is selected for display, or foranother purpose, the images near the selected image may still be madeavailable to a user (e.g., physician), which may, if desired, selectalso these images, for example for review (e.g., as a video clip, as a‘collage’, etc.), or for a processor, for example to manipulate theentire image subgroup related to the selected image.

Using image characteristics as an object may enable defining multipleimage subgroups first and, then, to select images by manipulating theirgeneral scores, for example in the way described in connection withFIGS. 6A-6B and FIG. 7 . Using imaged objects as an object, asdescribed, for example, in connection with FIG. 9 , may enable selectingan image first, and, then, to use the selected image to define ‘its’subgroup in order to nullify its entire subgroup before a next image isselected, and so on. Regardless of whether a subgroup of images is foundfirst and then an image is, or may be, selected from that subgroup, orwhether an image is selected first and then its subgroup is formed, thegeneral scores (GSs) of each subgroup are manipulated (e.g., nullifiedor modified) before a next image is selected.

FIG. 9 shows a method of selecting images for display (or for any otherpurpose) according to another example embodiment of the invention. Atstep 910 the value of a loop index n is initially set to zero (n=0). Atstep 920 a processor may receive, or retrieve, a group (a series) ofconsecutive images that originate from a GI tract of a subject and maycalculate a general score (GS) for each image. The processor may use anyof the methods described herein or other methods to calculate thegeneral scores (GSs) for the images.

At step 930, the processor may identify the greatest GS (a MGS)) amongthe GSs, and, at step 940 the processor may select (or mark), forexample for display, the image whose OS is the MGS, and set the value ofn to 1 (n=n+1=1) to indicate that, at this stage, one image has beenselected.

At step 950, the processor may check whether the value of n is equal toN (N being any preferred maximum number of images that is allowed to beselected; e.g., N=10, 25, 100, etc.) If the number of images that werealready selected is equal to N (this condition is shown as “Yes” at step950), the images selection process is terminated. However, if the numberof images that were already selected is (still) lower than N (thiscondition is shown as “No” at step 950), at step 960 processor nullifies(e.g., sets to zero) the value the MGS of (related to) the selectedimage, and, in addition, the processor may identify an object ofclinical importance (e.g., polyp) in the selected image, (it is assumedwith high level of confidence that an image includes such an objectbecause its OS is currently the greatest.)

At step 970, the processor may search for the same object of clinicalimportance (the object that is the same as or identical to the objectthat has been identified in the selected image) in images that are nearthe currently selected image. For example, the processor may apply atracking mechanism to try identify near images, on both sides of theselected image (preceding images and nu subsequent images), whichcontain a same object (e.g., a same pathology) for which the selectedimage was probably selected.

If the processor finds images near the currently selected image whichcontain the same object as in the currently selected image (thiscondition is shown as “Yes” at step 980), then, at step 990, theprocessor nullifies the GS of each image that contains the object.Nullifying the GSs of all the near images (images near the currentlyselected image) excludes the near images from the images selectionprocess, thus ensuring that the object identified (detected) in theselected image will be shown (or selected for any other reason; e.g.,for further processing) only once, that is, by only one, representative,image. However, if the processor does not find images ‘near’ thecurrently selected image which contain the same object as in thecurrently selected image (this condition is shown as “No” at step 980),then, at step 992, the processor may modify all the GSs that have notyet been nullified. The rational behind modifying the non-nullified GSsat step 992 is that if an image near the currently selected image doesnot contain the object for which the currently selected imager wasselected, the image near the selected image (the ‘near image’) maycontain another object that may warrant selection of this image.Therefore, the GSs of the near images may be modified (not nullified) inorder to give them, or to some of them, a chance to be selected as well.Modifying the non-nullified GSs at step 992 maybe implemented by usingany modification method that is described herein, or any other suitablemodification method. Step 992 (modifying non-nullified GSs) may beoptional. That is, the processor executing the embodiment of FIG. 9 mayskip step 992 and proceed to step 930 in order to commence anotheriteration or repetition 994. In other words, in some embodiments theprocessor may not modify general scores but, rather, identify another (anext) greatest GS, select the related image, nullify its ‘neighboring’(near) GSs (if there are near images that contain the same object as therelated selected image), then, again (at step 930), identify the nextcurrently greatest GS among the non-nullified GSs, and so on (proceedingwith steps 940, 950, and so on).

At each iteration or repetition (994) step 970 may result in a subgroupof images that includes a selected image, as a base image, and imagesthat are near the base image and include the same object. An imagesubgroup may sometimes include one image, which is the selected image,or a plurality of images.

After images that contain the same object as the currently selectedimage are nullified (per step 990), or after all non-nullified GSs aremodified (if step 992 is not skipped by the processor), anotheriteration, or loop repetition 994 may commence, at step 930, in order toselect another image. That is, at step 930 the processor may identifythe current (a new) greatest OS (a current/new MGS) among the GSs, oramong the modified GSs, then select, at step 940, the image related tothe WIGS, then at step 960, nullify the current MGS and identify a newobject (an object that is different than the object contained in thepreviously selected image(s), then, at step 970, search for the same newobject in images that are near the newly selected image, and so on.

An image that includes a same object as in a selected image does notnecessarily mean that the object is in the same location or has a sameorientation in these images. For example, a same object may be displacedbetween adjacent images, and/or the object m one image may be rotatedrelative to the object image in the adjacent image. The processor mayuse any known image processing method to determine whether two imagesshow, or contain, a same object (e.g., a same polyp), Such techniquesare well known in the art.

FIGS. 10A-10C demonstrate an image selection process in accordance withan example embodiment. FIG. 10A shows an example image group 1000including twenty eight contiguous images captured by an in-vivo deviceincluding one imager, and their respective general scores (GSs). (The 28images are shown ordered temporally, according to the time of theircapture, with image number ‘1’ captured first and image number ‘28’captured last.) In practice, the number of images in an image group maybe very large (e.g., tens of thousands), however, as in the otherdrawings, the small number of images shown in FIG. 10A is used only forillustrative purpose.

FIG. 10A will be described in association with FIG. 9 . After a generalscore is calculated for the twenty eight images, for example by aprocessor, (per step 920), the processor may identify the currentlygreatest GS (a current MGS), at step 930. Referring to FIG. 10A, thecurrently (at this stage the first) greatest GS, or MGS, is the GS ofimage number ten. (The value of the OS for image number ‘10’ is 93, asshown at 1010.) Therefore, the processor selects (per step 940) imagenumber ‘10’ because it is related to the current MGS. Assume that it iswanted to select more images (e.g., 35 images). Accordingly, theprocessor proceeds the images selection process by nullifying, at step960, the MGS (e.g., by setting its value to zero, as shown at 1020 inFIG. 10B), and by identifying an object in the selected image (also atstep 960).

Then, at step 970 the processor searches for the same object in imageswhich are near the selected image. Referring to FIG. 10A, assume that,by way of example, images numbers ‘8’ and ‘9’ (two images that precedeimage number ‘10’, the currently selected image) and images numbers,‘12’, ‘13’ and ‘14’ (four images subsequent to image number ‘10’)contain the same object as image number ‘10’. (In other words, all theimages that make up or form image group 1030 contain the same object.)Therefore, at step 990, the processor nullifies the GSs of all theimages in image group 1030, due to all of them containing the sameobject, so only one, representative, image should be selected. Thenullified values of these GSs are shown in FIG. 10B. For example, whilethe value of the GS of image number ‘8’ is 70 (in FIG. 10A), its valuein FIG. 10B (that is, after nullifying it in step 990) is zero.Nullifying the GSs of image number ‘10’ and the GSs of the images nearimage number ‘10’ excludes the entire image subgroup 1030 from the imageselection process, except for image number ‘10’, which is selected as arepresentative image of this image subgroup because, at this stage (atthis algorithm repetition), it has the greatest GS.

After the first image group (image group 1030) is excluded from theimage selection process, the processor identifies, again at step 930,the next MGS, which has, in the example of FIG. 10B, the value 82 (thisGS was calculated for image number ‘20’ and is shown at 1040). As withthe previous MGS, the processor selects image number ‘20’ at step 940,and nullifies the value of its GS at step 960. The nullified MGS value1040 of image number ‘20’ (the second selected image) is shown at 1050.By way of example, assume that only two images (image numbers ‘21’ and‘22’) that are near the currently selected image (image number ‘20’)include a same object as image number ‘20’. Therefore, the entire imagesubgroup 1060 is excluded from the image selection process-image number‘20’ for already being selected due to its OS being the second greatestGS, and image numbers ‘21’-‘22’ due to them containing a same object asimage number ‘20’.

FIGS. 10A-10C demonstrate that, with each iteration or repetition 994 ofthe embodiment of FIG. 9 , another subgroup of images may be detractedor removed from the image selection process. A third iteration orrepetition 944 (FIG. 9 ) may be applied to the remaining GSs in FIG.10C; e.g., to the GSs remaining after the exclusion of image subgroups1030 and 1060. If a selected image does not have any near image thatincludes the same object, the next image may be selected in a similarway without modifying the non-nullified GSs (by skipping or ignoringstep 992), or by modifying the non-nullified. GSs, per step 992.

The image selection methods disclosed herein may be implemented invarious ways, for example by using various score modificationmechanisms. For example, the modification function used to modify scoresmay be selected from a group consisting of; symmetrical function orasymmetrical function (‘symmetrical’ and ‘asymmetrical’ are with respectto already selected image(s)), linear function, non-linear, Gaussianexponent, exponent with an absolute value of distance, a step function,and a triangularly shaped function with its apex located at a selectedimage and linearly decreases as a function of distance. The modificationfunction may be selected according to a parameter selected from a groupconsisting of: the type of pathology, location in the GI tract andvelocity of the in-vivo device in the body lumen. For example, the imageselection process may be applied to all the images captured by thein-vivo device, and the process may include application of the samemodification function to the related MGSs; the image selection processmay be applied to all the images captured by the in-vivo device, but theprocess may include application of different modification functions toMGSs that are related to images captured in different portions, orsegments, of the body lumen; the image selection process may be appliedonly to images captured by the in-vivo device in a particular portion orsegment of the body lumen, and the process may include application ofthe same modification function to the related MGSs, or differentmodification functions; a modification function may be speed-dependentin the sense that when the in-vivo device moves in the GI tract at a lowspeed, suppression may be applied only to, or more rigorously to, MGSsthat are related to images which are relatively close to a selectedimage, and when the in-vivo device moves in the GI tract at a highspeed, suppression may be applied only, or more rigorously, to MGSs thatare related to images which are relatively far from a selected image;when the in-vivo device produces, for example, two series of images (onefor each imager), if one imager takes a picture of a pathology (e.g.,polyp), images from the other imager, which are close in time to thatimage may be suppressed significantly because there is no point inselecting additional images of the same pathology. (Images of the samepathology may be taken, for example, when the in-vivo device movesslowly, or when the images capturing rate is high comparing to themovement of the in-vivo device, and/or when the two (or more) imagers ofthe in-vivo device take a picture of the same pathology.)

In some embodiments, MGSs that are calculated for images related to oneimage group may be suppressed also with respect to images that arerelated to MGSs calculated for images that are related to another imagegroup. For example, if there are two imagers, one imager may be lookingin a forward direction (a direction of movement of the in-vivo device)and the other imager may be looking backward (in the oppositedirection). Images captured by both imagers may be ‘registered’ betweenconsecutive images in order to determine which imager is ‘zooming in’and which one is zooming out. Then, an unsymmetrical modificationfunction may reject images taken in the same GI site by the other head.

While, data gathering, storage and processing are performed by certainunits, the system and method of the present invention may be practicedwith alternate configurations. For example, the components gathering andtransmitting image data need not be contained in a capsule, but may becontained in any other device that is suitable for traversing a lumen ina human body, such as an endoscope, stent, catheter, needle, etc.

The foregoing description of the embodiments of the invention has beenpresented for the purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed. It should be appreciated by persons skilled in the art thatmany modifications, variations, substitutions, changes, and equivalentsare possible in light of the above teaching. It is, therefore, to beunderstood that the appended claims are intended to cover all suchmodifications and changes as fall within the true spirit of theinvention.

The invention claimed is:
 1. A computer-implemented method for selectingimages including clinically important information from images capturedby an in-vivo device, the method comprising: receiving a group ofcontiguous images captured in a body lumen, wherein with each image isassociated a general score, GS, indicative of a probability that theimage includes at least one type of pathology; and upon a user action:(i) iteratively selecting images from the images captured by the in-vivodevice based on their related GS and until a predetermined criterion ismet, wherein the images are selected one at a time; (ii) each time animage is selected, suppressing the GSs of images of the group ofcontiguous images, wherein the suppressing is performed such that theprobability of other images including clinically important informationto be selected is increased, wherein the clinically importantinformation in the other images relates to other pathologies or to otherlocations in the body lumen with respect to the pathologies or locationsshown in the selected image, correspondingly; and (iii) displaying orfurther processing at least a subset of the selected images.
 2. Themethod as in claim 1, wherein: the selecting of the images comprises,for each image selection iteration: identifying a greatest GS among theGSs; and selecting the image related to the greatest GS.
 3. The methodas in claim 1, wherein the criterion is a number of images or a GSthreshold or a combination thereof.
 4. The method as in claim 1, whereinsuppressing the GS of images comprises nullifying the GS related to theselected image.
 5. The method as in claim 1, wherein the suppressing ofthe GSs comprises suppressing the GSs based on a distance d betweentheir related images and a selected image.
 6. The method as in claim 5,wherein the smaller the distance d is, the greater a reduction in thevalue of the particular GS is.
 7. The method as in claim 5, wherein thedistance d between an image related to a GS and a selected image iscalculated or measured in units of: (i) time, or (ii) a number ofintervening images, or (iii) a number of intervening image subgroups, or(iv) a distance that the in-vivo device travelled in the body lumenwhile taking the images, or (v) a distance that the in-vivo devicetravelled in the body lumen with respect to a landmark in the group ofimages or in the body lumen, or (vi) a percentage of a video clipincluding all, or most of, the group of contiguous images.
 8. The methodas in claim 7, wherein each unit instance is calculated or measured as:(i) an absolute value calculated or measured relative to a beginning ofthe group of contiguous images, or relative to a beginning of the bodylumen, or relative to a landmark in the group of contiguous images or inthe body lumen, or as (ii) a percentage or fraction of the group ofcontiguous images or body lumen, or (iii) a percentage or fraction of avideo clip including all, or most of, the group of contiguous images, or(iv) a percentage or fraction of a distance travelled by the in-vivodevice, or (v) a percentage or fraction of a time travelled by thein-vivo device.
 9. The method as in claim 1, further comprising for eachimage of a plurality of images of the group of contiguous images:identifying an object in the image; and searching for an object which isthe same as the identified object in images near the image, wherein theimage and its near images which include the object form a subgroup. 10.The method as in claim 9, wherein: the plurality of images are theselected images, the suppressing of the GSs of images of the group ofcontiguous images for each selected image comprises nullifying theimages of the subgroup related to the selected image, and the selectingof the image comprises: identifying a greatest GS among the GSs; andselecting the image related to the greatest GS.
 11. The method as inclaim 9, wherein: the object is image characteristics, at least aportion of the group of contiguous images comprising the plurality ofimages is divided into the subgroups, and the selecting of an imagecomprises: identifying a maximum general score (“MGS”) for eachsubgroup; selecting an image associated with a greatest MGS (MGS|max)among the MGSs; and nullifying the greatest MGS relates to the selectedimage.
 12. The method as in claim 1, further comprising enabling theuser to select one of: the type of pathology included in the selectedimages, the location of the selected images in the body lumen, thenumber of images to be selected, a modification function for suppressingthe images according to the part of the body lumen from which imageswere taken, or a combination thereof.
 13. The method as in claim 1,wherein the suppressing comprises applying a modification function tothe GSs with respect to a selected image.
 14. The method as in claim 13,wherein the modification function is selected according to a parameterselected from the group consisting of: type of pathology, location ofthe in-vivo device in the body lumen and velocity of the in-vivo devicein the body lumen.
 15. The method as in claim 13, wherein applying amodification function to a particular GS comprises identifying a minimumdistance, Dmin, between the image related to the particular GS and anyof the selected images, and modifying the particular GS using theminimum distance, Dmin.
 16. The method as in claim 1, wherein thesuppression comprises applying a modification function selected from agroup consisting of: symmetrical function, asymmetrical function, linearfunction, non-linear, Gaussian exponent, exponent with an absolute valueof distance, a step function, and a triangularly shaped function withits apex located at a selected frame and linearly decreases withdistance.
 17. The method as in claim 1, wherein the body lumen is thegastrointestinal tract.
 18. The method as in claim 1, further comprisingzeroing out a GS of an image if the image is noisy.
 19. A system forselecting images including clinically important information from imagescaptured by an in-vivo device, the system comprising: a data processor,the data processor configured to, for a group of contiguous imagescaptured in a body lumen, with each image having associated therewith ageneral score, GS, indicative of a probability that the image includesat least one type of pathology, perform upon a user action: (i)iteratively selecting images from the images captured by the in-vivodevice based on their associated GS and until a predetermined criterionis met, wherein the images are selected one at a time; (ii) each time animage is selected, suppressing the GSs of images of the group ofcontiguous images, wherein the suppressing is performed such that theprobability of other images including clinically important informationto be selected is increased, wherein the clinically importantinformation in the other images relates to other pathologies or to otherlocations in the body lumen with respect to the pathologies andlocations shown in the selected image; and (iii) displaying on a displayor further processing the selected images or a subset of the selectedimages.
 20. The system as in claim 19, wherein: the selecting of theimages comprises, for each image selection iteration: identifying agreatest GS among the GSs; and selecting the image related to thegreatest GS, the suppressing of the GS of images comprises nullifyingthe GS related to the selected image, and the criterion is a number ofimages or a GS threshold or a combination thereof.
 21. The system as inclaim 19, wherein the suppressing of the GSs comprises suppressing theGSs based on a distance d between their related images and a selectedimage.
 22. The system as in claim 19, wherein the suppressing comprisesapplying a modification function to the GSs with respect to a selectedimage.